Their goal is to “Democratize AI.” They have done so by open-sourcing the Stable Diffusion text-to-image model and are working on releasing other models, including large language models.
This approach is in stark contrast to the one taken by OpenAI, Facebook, Google, etc. These companies have gated access to ML models like GPT-3 via APIs or invite-only programs. The reasoning is that these models could be used to generate hateful text and images and are generally too dangerous to be released to the ignorant masses.
In a recent interview, Emad Mostaque, the CEO of Stability.Ai and a fascinating thinker, talks about the inevitability of generative and large language models leaking out to the wild. He wants to focus on giving people a framework for the ethical use of AI while giving them the tools to build and train models for their specific uses.
Stability.Ai has struck a deal with Eros Interactive to get access to their massive library of Indian content. Can you imagine what could be trained using that data?
Congratulations to Stability.Ai. I am curious about what this more open (or perhaps reckless?) approach to ML will bring us.
Image generated by Stable Diffusion – Prompt: “A happy robot drinking champagne at a cocktail party at night, oil painting, muted, candid, high resolution, trending on artstation”
For some, matrix multiplication may trigger memories of tedious high school algebra exercises. Last week, this humble mathematical operation was also the topic of a significant breakthrough in machine learning.
Art generated by Stable Diffusion
Background – Matrix Multiplication
Matrix multiplication is the foundation on which many core computational operations are built. Graphic processing, machine learning, computer gaming, etc. – all rely on matrix multiplication. At any given point in time, there are millions of computers doing (probably) billions of matrix multiplication operations. Making this humble operation faster would result in significant computational and efficiency gains.
Why do we want faster matrix multiplication?
Multiplying two matrices involves doing a large number of multiplication and addition operations. For example, multiplying a 4X5 and a 5X5 matrix involves 100 multiplication operations using the traditional matrix multiplication method that has been around since the early nineteenth century. In 1969, a mathematician, Volker Strassen, came up with an ingenious method that reduced the number of operations required by about 10%. This was hailed as a groundbreaking discovery in the world of mathematics.
DeepMind Enters the Arena
This brings us to DeepMind’s paper last week, where they used the AlphaTensor deep learning model to discover a new algorithm for matrix multiplication that is faster by about 10 – 20% than the Strassen method. This is a *colossal deal*! We are seeing a machine learning model find new algorithms to solve material, real-world problems. We have already seen DeepMind make groundbreaking discoveries in computational biology with AlphaFold. We now see applications of its Deep Learning models (based on playing games) to foundational aspects of the modern world. Exciting times are ahead!
TikTok* has caused political controversies, made Meta change its Instagram platform to mimic it, and caused many a moral panic. All signs of success.
TikTok’s use of machine learning to present a never-ending stream of engaging content is an example of the successful application of machine learning at a gargantuan scale.
But, as the linked WSJ article shows, TikTok’s growth is driven by massive investments in technology and advertising.
ByteDance, which owns TikTok, lost more than $7 billion from its operations in 2021 on $61.4b in revenues
The company spent $27.4b on user acquisition and $14.6b on R&D
I believe that the value of applied machine learning technologies will accrue to those companies that can deploy vast resources to acquire data (in TikTok’s case – users who generate the data) and build massive data and ML infrastructure. I am sure we will see similar revenue and spending trends if we analyze Meta and Google’s results.
While Data Science and Machine Learning careers grab the limelight, making ML platforms more efficient and processing data much cheaper will be more lucrative in the long term.
If a company spends significant cash on ML and data infrastructure, it will always look for people to make things more efficient. Possible careers for the future:
Natural Language Processing or NLP is a catch-all term for making sense of unstructured text-like data. Google search recommendations, chatbots, and grammar checkers are all forms of NLP. This is a field with many years of research. But, for the last 5-7 years, machine learning has reigned supreme.
Five years ago, machine learning approaches to NLP were labor intensive. Success meant having access to large amounts of clean and labeled training data that would train ML models. A text summarization model would be pretty different from one that did sentiment analysis.
The development of large language models or LLMs has revolutionized this field. Models like GPT-3 are a general-purpose tools that can be used to do several different tasks with very little training.
To show GPT-3 in action, I built a tiny slack bot that asks some questions and uses GPT-3 to generate actions. The video below is a demo of the bot and also an explanation of how to prompt GPT-3 to do NLP tasks.
The morning after a big software release can be both terrifying and exhilarating.
Kudos to the Ethereum team for pulling off a massively complex transition from Proof of Work to Proof of Stake. This reduces the energy consumption of the Ethereum blockchain by 99.95% (and global energy consumption by 0.2%).
I am still not convinced by the utility of the crypto ecosystem, and I am sure there will be bumps along the way. The transition to Proof of Stake will further entrench the power of those holding significant capital. Proof of Work meant influence aggregated to those who could deploy significant computing power by spending vast amounts of money on GPU hardware. The switch to Proof of Stake will remove the hardware intermediation layer. The massive savings in energy and speedups in transaction processing make it a worthwhile change.
Philosophical arguments aside, as a software engineer, I can appreciate a job well done 👏🏾👏🏾👏🏾.
I have played video games for thirty years. With two kids and a busy job, I don’t get as much time to play as I would like, but I pick up my Xbox controller whenever I get a chance. Over the last few months, this has meant playing Elden Ring, a role-playing game where you explore and adventure in a fantastical realm called the Lands Between.
Elden Ring – Bandai Namco
I am terrible at Elden Ring, yet I have spent hours playing it over the last six months. I am in awe of the game’s scale, beauty, and challenge.
When not playing video games, I support software development teams. Over the last ten years, I have worked as an Engineering Manager and, more recently, as a CTO at early-stage startups.
Managing and supporting teams is hard. You have to balance competing priorities and make decisions under conditions of ambiguity. Periods of stability can be interrupted by crises. It’s not that different from playing a game like Elden Ring!
As I reflected on why I enjoyed playing some video games more than others, I realized there are parallels between excellent video game design and supporting high-performance teams.
In this post, I explore what makes a video game great and what lessons we can apply from video game design to become better managers.
What Makes a Good Video Game?
Video game design is a vast and evolving topic. However, there are three critical elements to a good video game.
Good vs. Bad Video Design
The Story: What is this video game about, and why should I bother playing it?
A good video game story makes players want to invest their time in learning more about the world and the story. Games like Elden Ring, Horizon Zero Dawn, and God of War have stories that push players to do all sorts of side quests and missions. Exploring the world helps fill out the story, and each task moves the character and the story forward.
Hades – Supergiant Games
The Setting: Where am I going to be spending my time?
Seasoned gamers are familiar with the “one more turn” phenomenon. You want to keep playing because the game world is so darn fun. Dungeons filled with loot (and traps), exciting side missions, and beautiful scenery make the game’s exploration and progression fun. Games like the Mass Effect series make you care not just about the characters but also the broader game world and lore.
Gameplay Loop:How do I play the game?
Elden Ring is brutally difficult, yet I keep returning to the game. The reason is that while challenging, the gameplay is fair and predictable. And I get a real sense of accomplishment after clearing a particularly tricky dungeon or boss encounter. Hades is another game that has wonderfully compelling gameplay. Great video games have a simple yet addictive core gameplay loop. These are the actions that a player is expected to perform most often to make progress in the game. These must be balanced to avoid tedium while presenting fun and challenging experience.
From Video Games to High-Performance Teams
What do video games have to teach us about supporting high-performance teams?
We will approach this by looking at the same attributes that we explored for successful video games:
Story → Vision
Setting → Workplace
Gameplay → The Day-to-Day Work
Vision: Why am I being asked to do this?
A compelling narrative is about selling a vision – what will the player gain at the end of this quest line, boss battle, or challenging project? An honest, well-articulated vision helps give direction to a team. In his viral talk, “Start with Why,” Simon Sinek talks in detail about this “inside out” approach. Having a vision contradictory or inconsistent with the day-to-day work could lead to frustration and a lack of trust. The narrative must be straightforward and backed up with action aligned with the company’s stated values.
Workplace: Where do I spend my time?
A leader must create a workplace that maximizes productivity while allowing creativity, serendipity, and exploration. This is true both for in-person and remote work. Encouraging (reasonable) risk-taking and exploration enable more engaged and motivated teams. A video game with a predictable and tired setting (post-apocalyptic zombie infestations, for example..) is boring. Similarly, an environment that is dull or unpleasant is a drag on motivation and productivity. Psychological safety is also essential. As any player of online games knows, dealing with abuse and cheating makes for a miserable experience. A workplace perceived as hostile and a leader unwilling to support and protect their team will lead to people walking out of the door.
The Day to Day: How I do my work…
A manager must focus on the “gameplay” loop for their team. What are the challenges that stop them from doing their work? For software engineering teams, this could be the ease of making changes, getting pull requests approved, and getting changes into production. I have rage-quit lots of games because “life is too short.” Online games where I keep getting taken out by snarky teenagers with fast twitch reflexes are a particular bug-bear. Elden Ring can also veer into frustrating territory until I realized I could avoid most difficult encounters until I was leveled up and ready. When supporting a team, you need to consider what can be done to remove obstacles for your team. It may mean picking the right battles and knowing when to compromise. Making the workday loop engaging for your team is a critical leadership skill.
Conclusion – Gaming and Learning
Video games are the dominant entertainment and artistic form of our time. Oscar Wilde opined, “Life Imitates Art far more often than Art Imitates Life.” I agree.
Video games have been around far longer than modern software engineering tools such as Agile, DevOps, and other current paradigms. The art of video game design has been refined through decades of experimentation and many, many failures.
Indeed, as managers, most of us will be supporting teams that grew up playing video games. As a medium, video games create interactive, compelling worlds where people enjoy spending their time.
Taking cues from how video games are designed could help us become more effective supporters and advocates for our teams.
Managing and supporting a team is a difficult job. A manager is often a coach, disciplinarian, a surrogate parent, and cheerleader – all rolled into one. I am always on the lookout for ways to be a better supporter of my teams. Over the last few days I discovered a fortunate intersection in my interests in sport and in management.
I am a fan of the Arsenal football club. Like many other Arsenal fans, I have been watching and enjoying the Amazon Prime show “All or Nothing: Arsenal,” which follows Arsenal through the 2021 – 2022 season. We get a close look at how Arsenal’s manager Mike Arteta works with his players and his management team and motivates them over a challenging 45-game season.
At 38 years old, Arteta is currently the youngest manager in the English Premier league. He has been at the helm since 2019. The Arsenal squad also has the youngest average age in the Premier League – this season, the first team averages just 25.2 years old.
Arteta’s reign has seen the club slump to 8th place in the 2019 and 2020 seasons before having a marked improvement in form to finish 5th in 2021.
Arteta comes across as an intense, detail-oriented and hands-on manager. I realized that Arteta’s approach to management was something I had come across before. It is strikingly similar to that described in Radical Candor by Kim Scott – one of my favorite books on building high-performance teams.
In this post, I will summarize the Radical Candor approach through the lens of Arteta’s unique take on people management.
What is Radical Candor?
Radical Candor is a book by Kim Scott published in 2017. It focuses on creating a culture of guidance, building an effective and cohesive team, and driving results collaboratively.
The book’s central thesis is that effective leadership requires direct, clear, truthful, and kind feedback, even when difficult. Scott believes getting to know each person in your team personally is essential to understanding their desires and motivations.
The book offers tactical and strategic advice to leaders on building high-performing teams in an open, healthy, and productive environment. I strongly recommend Radical Candor for those looking for an authentic and modern approach to people management.
We see Arteta speaking candidly and passionately with his players throughout the season. He is generous in his feedback when things go well. When things go poorly, Arteta is direct, passionate, and emotional. While he doesn’t mince words, he doesn’t humiliate his players in the dressing room or in front of the media.
Caring Personally while Challenging Directly
The 2X2 below shows “Radical Candor” as giving feedback by caring personally while challenging directly. It also covers some dysfunctional ways of giving feedback – obnoxious aggression, ruinous empathy, and manipulative insincerity.
From Radical Candor by Kim Scott
Ted Lasso aside, football managers are not known for their empathy. Indeed, the likes of Sir Alex Ferguson are revered for their ability to drive performance through aggression and intimidation. Ferguson’s proverbial “hairdryer treatment” would probably end up in the “Obnoxious Aggression” quadrant above.
While he is partial to the odd F-bomb, Arteta’s open displays of emotion and vulnerability inspire his players, as seen in this clip. At the end of a run of poor results in April at Crystal Palace and Brighton, we see a manager who cares about the results and is passionate about wanting to make things better. He calls out a lack of intensity from his players and gives specific feedback on the training pitch and in the dressing room.
This combination of caring personally and directly challenging poor performance is right out of the Radical Candor playbook.
Building Resilience Through Trust
The Radical Candor approach is built on a foundation of trust. Trust is difficult to gain and easy to lose. The key to building trust is to be transparent and authentic, clear and concise in communication, and consistent in your actions.
Arteta calls out his “non-negotiables” in explaining his management philosophy: respect, commitment, and passion. Throughout the show, we see Arteta embodying these values.
This results in significant friction with his star player Pierre-Emerick Aubameyang who does not meet Arteta’s high expectations around discipline and accountability. Aubameyang is the club captain and is a popular member of the squad.
Arteta ends up stripping Aubameyang from the captaincy of the team. This could have destabilized the team, but it seems to have the opposite effect. Arteta does not criticize Aubameyang, and his team is made aware of how important trust and accountability are to their manager. By showing consistency in his actions and clarity in his communication, Arteta builds trust and resilience, resulting in outstanding results on the pitch in the second half of the season.
Managing Rockstars and Superstars
In Radical Candor, Scott describes Rockstars as stable employees who are happy and effective in their roles. These are folks who are aware of their talents and limitations and can consistently perform at a high level. On the other hand, Superstars are on a steep career trajectory and can be change agents. They are ambitious and want new opportunities. A high-performing team usually has both rockstars and superstars.
Given his young team, Arteta works with plenty of players on steep growth trajectories. Bukayo Saka, Emile Smith Rowe, and Eddie Nketiah are all young and eager to learn and perform at the highest level. However, he also has players like Rob Holding and Mohammad Elneny. While experienced pros, they have specific roles and are not guaranteed a place in the starting lineup. Holding and Elneny are the rocks (and Rockstars) that provide a stabilizing influence in the dressing room and on the pitch while laying a foundation for the more flamboyant players up front.
As a manager, Arteta has to ensure that the players like Holding and Elneny feel valued and are ready to perform when called upon while the ambition and talents of the young Gooners are nurtured. You can see this come together towards the end of the season. Holding and Elneny perform well after being called into the starting eleven after injuries. He also gives the ambitious Nketiah an extended run. He repays his faith by scoring five goals in the last seven games. Arteta and his team need to understand each player’s mentality and ensure they feel motivated to perform when needed.
Conclusion
All or Nothing is entertainment and has been edited to push a narrative and maximize engagement. Mikel Arteta has come under intense criticism for being uncompromising and stubborn at times – especially with how he has managed high-profile players like Aubameyang and Mesut Ozil. But, the little glimpse we get in the documentary shows a young manager trying to build a successful team.
Plenty of books like Radical Candor have come out of Silicon Valley, and the content often reflects the author’s experience working in technology companies. The strength of a book, especially in the crowded management genre, is how applicable the message is across different domains.
Managing Arsenal presents quite different challenges from managing a software engineering team. However, I hope the lessons of Radical Candor and All or Nothing are valuable to managers looking to build and support a high-performance team.
Machine Learning has brought huge benefits in many domains and generated hundreds of billions of dollars in revenue. However, the second-order consequences of machine learning-based approaches can lead to potentially devastating outcomes.
This article by Kashmir Hill in the New York Times is exceptional reporting on a very sensitive topic – the identification of abusive material or CSAM.
As the parent of two young children in the COVID age, I rely on telehealth services and friends who are medical professionals to help with anxiety-provoking (yet often trivial) medical situations. I often send photos of weird rashes or bug bites to determine if it is something to worry about.
In the article, a parent took a photo of their child to send to a medical professional. This photo was uploaded to Google Photos, where it was flagged as being potentially abusive material by a machine learning algorithm.
Google ended up suspending and permanently deleting his Gmail account and his Google Fi phone and flagging his account to law enforcement.
Just imagine how you might deal with losing both your primary email account, your phone number, and your authenticator app.
Finding and reporting abuse is critical. But, as the article illustrates, ML-based approaches often lack context. A photo shared with a medical professional may share similar features to those showing abuse.
Before we start devolving more and more of our day-to-day lives and decisions to machine learning-based algorithms, we may want to consider the consequences of removing humans from the loop.
Feedback is an integral part of working in a team and managing people.
Code reviews, architectural reviews, 1-1s, and Sprint Retrospectives, are all situations that involve giving (and receiving) feedback as software engineers, product managers, and engineering managers. Yet, giving critical feedback can be a difficult and stressful experience. So how best to navigate these potentially adversarial situations?
George Saunders is one of my favorite contemporary writers. He has an excellent Substack called Story Club. In this week’s post, Saunders talks about giving feedback to other writers. While his advice is in the context of a writers workshop, I found it quite applicable to my work.
Saunders advices us to give specific yet kind feedback:
.. as we learn to analyze and diagnose with increased specificity and precision, the potential for hurt feelings diminishes, because we are offering specific, actionable ways (easy ways, often, ways that excite the writer, once she’s made aware of them) to make the story better. And who doesn’t want some of that?
George Saunders
Giving constructive or critical feedback is integral to working as a software engineer. Yet, these conversations can become challenging.
One might be tempted not to say anything or speak in the most generic and broad terms to avoid offense. Instead, as Saunders suggests, the focus should be on giving thoughtful, specific, precise, and actionable feedback:
In this [giving feedback], we indicate that we are on the writer’s side, we are rooting for her and are glad to have found these small but definite ways to make her story better. There’s no snark, no competition, no dismissiveness, nothing negative or accusatory about it; just the feeling that we, her readers, are coming together with her, the writer, by way of craft. We’re all on the same team, the team of art.
Stuart Kirk, former journalist for the FT and now former banker at HSBC got into trouble last week for suggesting that climate change risks are overblown.
Before his suspension, he was the head of Responsible Investing for HSBC asset management.
The “markets”, in all probability, already priced in climate change risks
Climate change adaptation is more pragmatic and likely cheaper than mitigation
By the time climate change hits, we will all be dead anyway. So why bother?
I found the presentation interesting and a little horrifying – in the drunk uncle holding forth – sense. He makes some good points – about the short term nature of markets and investing, about the necessity of climate change mitigation, for example. But the general attitude can be summarised as 🥱🤷♂️.
I am still surprised that after 2008, after COVID, Ukraine and all the other shocks, people like Mr Kirk still think in terms of normal distributions. I.e. the probability of events can be modelled as a bell curve – with very bad or very good events having low probabilities, and predictable “average” events being the most common.
Or to channel mathematician, philosopher and truculent Twitter warrior N. Taleb, the likes of Mr Kirk believe that the impact of climate change to be an ergodic process while it is most definitely not.
Doing a Google search for “Ergodicity” will lead you to baffling mathematical and statistical explanations. But it is, at its core, an intuitive concept. In a non-ergodic system, things that are true for the aggregate may not be true for the individual.
In Mr Kirk’s presentation he plots economic growth from the 1930s to the present day and states, pretty much, that the “line goes up” despite world wars, economic upheaval, recessions etc. He uses this trend to assert that we will be fine despite the risks of climate change. The benefits of a growing economy will overcome the downsides of climate change.
However, the story of aggregate growth over the last 100 years hides tales of individual ruin.
For example, someone who invested all their savings in tech stocks in 2002 probably didn’t have anything left to make money when the market finally moved up. For those unlucky investors, it was game over. Therefore, we are modelling a process that is non-ergodic (individual outcomes can be radically different than aggregate outcomes) as an ergodic process.
So, what does this have to do with climate change?
I believe that the effects of climate change make our economic system even more non-ergodic. It makes it much more likely to have extreme events – heat waves, wild fires, hurricanes, droughts, etc. This makes modelling based on aggregate probabilities a little suspect. Sure, you could increase insurance premiums for coastal communities to account for higher flooding risk. This is what Mr Kirk means by the risk being “priced in”. But what happens when entire communities are wiped out due to an unprecedented storm surge, or heat wave, or forest fire?
Climate change adds more chaos to a complex system. It heightens the likelihood of extreme events that have catastrophic outcomes. Adaptation measures are necessary but they will do little to mitigate the impact of “black swan” events. So it doesn’t matter how complex your modelling is, and how sophisticated your investment strategy is. If you die due to a freak hurricane, you are done.
The likes of Mr Kirk are making a category error. The only way to “win” in an non-ergodic system is to survive. We should be thinking of what can be done to ensure that we don’t face catastrophic loss, so that we can continue to reap the benefits of growth in the future.
