Focus on what's just around the bend, not miles down the road.
Focus on what's just around the bend, not miles down the road.
Save on your AI spend year-round
The latest issue of Complex Machinery: when the computer's records don't match reality
The latest issue of Complex Machinery: GenAI is in its Geocities moment. Shocking bots. And learning to be quiet.
Contrary to popular belief, data barriers are a good thing
To fix your company's AI problems, start at the top.
The latest issue of Complex Machinery: The bot doesn't always have to talk
You didn't build it yourself, so you have to take some things on faith.
To borrow a phrase about the cloud ...
The latest issue of Complex Machinery: AI is magic. In various meanings of the word.
The latest issue of Complex Machinery: Sometimes AI is a better fit for that task. Emphasis on "sometimes"
The latest issue of Complex Machinery: Machines conquered Wall Street, then learned to play a mean game of poker.
A new perspective on risk management
The latest issue of Complex Machinery: AI use cases, as driven by fraud and stunts. And squeeze bottles.
What every middleman can learn from OnlyFans
I wonder how things are going at LinkedIn HQ these days?
LinkedIn has default its genAI data collection to opt-in
You can't copycat the FAANGs' data practices
Share the story of your worst meal ever
What can you learn from a successful YouTube channel?
The power to build
Thoughts on a recent episode of Intelligence Matters: The Relaunch
A person with technical know-how can ulso understand how a business operates.
Have I gone post-technical?
What the model doesn't know, can hurt you
The latest issue of Complex Machinery: Cookies can tell us a lot about AI use cases. Especially the more questionable variety.
It helps to know the difference
The fall of Enron created an interesting and real-world dataset for text mining
Someone who attended the BlackHat conference left with concerns about LLMs
Using machines to say 'no'
When a company bypasses its own data safeguards
I've said it before, and I will have to say it several more times
The latest issue of Complex Machinery: More lessons from the CrowdStrike incident
A broad skillset goes a long way
This is a good time to move at your own pace
Wait, two _trillion_ dollars??
A reminder of what makes AI projects possible
In AI, as elsewhere, execution is what matters
The latest issue of Complex Machinery: How the CrowdStrike incident reflects a key risk in complex systems
The latest issue of Complex Machinery: The shady side of the AI business.
The latest issue of Complex Machinery: Understanding the race against time that is AI's popularity.
I've been posting elsewhere...
Executives want to learn about AI. I can help.
The latest issue of Complex Machinery: Pondering the easy money in AI, and when it will come due.
The latest issue of Complex Machinery: Google changes path on its AI Overviews product
The latest issue of Complex Machinery: the new GPT-4o trips, Google's AI Overview stumbles, and asking what-if questions when building AI products
Your company's plans for data may not going as expected. Here's what you can do.
Is AI having its bubble moment? If so, why would that matter?
The latest issue of Complex Machinery: Some short-term AI annoyances, plus some optimism about the future
The latest issue of Complex Machinery: Pricing out the risk of your latest AI adventure
random thoughts and articles from the past week
random thoughts and articles from the past week
The latest issue of Complex Machinery: A look into auto manufacturers passing driver details to data brokers
random thoughts and articles from the past week
random thoughts and articles from the past week
The latest issue of Complex Machinery: thinking about our approaching bot-on-bot future
random thoughts and articles from the past week
random thoughts and articles from the past week
The latest issue of Complex Machinery: the randomness inside every AI model
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
The latest issue of Complex Machinery: deepfakes for crime, facial recognition, and minding the robots
random thoughts and articles from the past week
Complex Machinery is my new newsletter on AI, risk, and related topics.
random thoughts and articles from the past week
AI chatbots are great, but they're still a little rough around the edges
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
Let's remember the human element in AI-driven content moderation
Understanding table stakes for an AI modeling effort
random thoughts and articles from the past week
Wider AI truths, as surfaced by LLM failures
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
What the stock photo company's new service tells us about safe-to-use datasets
random thoughts and articles from the past week
What's the next step in AI?
Seven questions to help you improve your training data.
random thoughts and articles from the past week
A recent Mozilla report about modern cars highlights privacy concerns
random thoughts and articles from the past week
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random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
It sounds like something more than a simple cash-grab
Lessons from an AI chatbot's terrible recipe ideas.
random thoughts and articles from the past week
Reflections on operational risk, in light of the anniversary of the Knight Capital meltdown
random thoughts and articles from the past week
random thoughts and articles from the past week
The risks and rewards of using vendor APIs for generative AI models
random thoughts and articles from the past week
Success in AI requires that you learn some things and unlearn others.
random thoughts and articles from the past week
We have off-the-shelf models and turnkey data tools. Why do you need to hire data scientists, then?
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random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
A reminder that risk and reward are a package deal
random thoughts and articles from the past week
A handful of all-too-common pitfalls in hiring.
Releasing an AI chatbot exposes your company to new risks. Here are some ideas on how to handle them.
random thoughts and articles from the past week
Making the case for an experienced generalist with software skills.
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
Having trouble filling those data roles?
random thoughts and articles from the past week
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random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
If you want the most out of AI, you need to be strategic about how you employ it.
random thoughts and articles from the past week
A reminder of generative AI's chaotic potential
Using automation to double down on an ineffective metric.
random thoughts and articles from the past week
The artist currently and formerly known as "AI" gets another turn on the stage.
A stakeholder's view of how data scientists build and deploy ML models.
random thoughts and articles from the past week
random thoughts and articles from the past week
random thoughts and articles from the past week
What custom software can tell us about bringing AI into a company.
Operating on bad metrics is worse than having no metrics at all.
My search for the major use case for web3
random thoughts and articles from the past week
Asking "why?" more often.
Some metrics are good, some are bad. But even the good ones can be bad if you're not careful.
My take on tools such as Dall-E, Stable Diffusion, and ChatGPT
random thoughts and articles from the past week
Risk mitigation for your ML/AI projects
Automated machine learning (autoML) and its impact on the ML/AI space
A short list of ways an ML/AI modeling project can go off the rails
Is it time to update your ML models?
A quick on the FTC's recent proposal, and a link to something I wrote almost a decade ago
Why this question deserves a deeper conversation.
The video IS the advert
In light of the recent layoffs in the big-named tech firms
Using spreadsheets to explain core ML/AI concepts to executives.
Measure what matters
Disney+ has announced an ad-supported tier. Here's my idea on how they might run it.
An update on my web3 newsletter
The second article in a short series on N-sided marketplaces
I've launched a newsletter covering web3: cryptocurrencies, blockchain, and metaverse
N-sided marketplaces are very common in the business world. What are they, and how do they work?
Some datasets are problems in waiting.
Potential problems that can affect the entire field.
Adopt a popular expression to improve your risk awareness and assessment.
Sometimes, you have to tackle a challenge head-on.
Keeping the bigger picture in mind.
You'll see fewer posts here for a while, as I pursue some new projects
The second part of my interview with product manager Chris Butler, this time on communal computing and AI.
My panel discussion with Linda Liu (Hyrecar) and Giacomo Vianello (Cape Analytics)
Zillow recently closed down its home-buying program. Do I see this as a failure of ML/AI? In a word: No.
I've published an article on O'Reilly Radar: The key ingredient to a successful remote team? Leadership buy-in.
I interviewed product manager Chris Butler about the role uncertainty plays in AI product management.
Best practices to balance the risk and reward of building predictive models.
Spotting opportunities to build AI systems that complement, not outright replace, people on the job.
BI is periods. AI is question marks. Simulation is ellipses.
When talking with your company's data scientists, does the conversation quickly bog down? Try these questions to keep things moving.
Want to improve your risk assessment? Identify, then question, the constants in your world.
Nervous about meeting with your company's execs, legal counsel, or product team? Just answer these four questions.
I've published an article on O'Reilly Radar: how many times will we rename the data field?
Three steps can reduce churn in your company's data leadership role(s).
Humans versus machines? To reduce your risk, the best answer is "yes."
As we learn more about AI, what will change about how we develop and deploy it?
Learning how to handle risk, from the fields that do it well.
Every company needs someone to be their extra eyes.
An introduction to risk.
In search of ML/AI success? Know your hard and your soft numbers.
Does the AI hype meet the technical term of a "bubble?"
Putting AI to good use in a dangerous environment.
Lemonade's recent media spotlight is a cautionary tale for any company using ML/AI.
Discipline pays off.
There's a lot more to this than just building models.
A little planning will go a long way.
Go off the beaten path to make the most of your data-related hiring.
Is your company getting started with ML/AI? These uncommon tips will save you time and trouble.
Following the herd can be costly. Improve your ML/AI shop by following these undervalued practices.
It's not just train-test-deploy
What nighttime warfighting can teach us about using AI in companies.
Why it's important to be able to simulate your own data.
Determine whether an ML/AI project is for automation or for innovation, so you can prioritize it accordingly.
Punctuation matters when working with data: BI is periods. AI is question marks.
Understand which aspects of your ML/AI shop can (and cannot) give you an edge over the competition.
If your company has several ML/AI efforts on the roadmap, it can be difficult to decide how to prioritize them. You can look to the stock market for guidance.
I've published an article on O'Reilly Radar, on what makes a good question and what are my favorite questions to ask.
It's bad enough when the model is wrong; even worse, when it's wrong and you didn't have to build it in the first place.
Explaining the realities of how an ML/AI project may go awry.
Shedding light on the hidden costs of employing ML/AI models, which can upend the price/value ratio.
I've published a new article on O'Reilly Radar, on how the Covid-19 pandemic influences how we think, spend, and manage our businesses.
When a group predicts students' performance in lieu of holding a exam, it leads to some lessons in the deployment and use of ML/AI models.
What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 9 in a series)
What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 8 in a series)
What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 7 in a series)
ML/AI models still require a lot of human involvement.
What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 6 in a series)
What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 5 in a series)
What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 4 in a series)
What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 3 in a series)
What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 2 in a series.)
What can the world of algorithmic (electronic) trading teach us about good ML/AI practices? (Part 1 in a series)
Three questions to help you gauge your risk of being laid off.
Business models involving ML/AI are sitting on unrecognized risk.
COVID-19 may be what gets companies to take their ML/AI efforts seriously.
The AI world is sitting on all kinds of risk ... but no one wants to talk about it.
Should your company's data scientists hold a PhD? Probably not.
Executives want to know how to employ ML/AI in their company. They need more than just quick tips.
Since "more data is better," what do I do if I don't have enough?
How much data do you need to build good predictive models?
Looking for a data science job? It involves far more than the technical know-how.
Looking at data ethics through the lens of risk. (Part 5 in a series.)
Looking at data ethics through the lens of risk. (Part 4 in a series.)
Looking at data ethics through the lens of risk. (Part 3 in a series.)
Looking at data ethics through the lens of risk. (Part 2 in a series.)
Looking at data ethics through the lens of risk. (Part 1 in a series.)
How can business intelligence (BI) launch your data efforts, and pave the way for your first data science hire?
Having trouble hiring data scientists? or, once you hire them, do they not stick around? You may be tripping over your own feet. Part 2 of 2.
Having trouble hiring data scientists? or, once you hire them, do they not stick around? You may be tripping over your own feet. Part 1 of 2.
Having trouble hiring data scientists? Borrow some ideas from your sales team.
A successful data science shop requires more than just data scientists.
The what, why, and how of a data strategy -- a road map for your company's data efforts
Stack the deck in your favor when hiring people into your data team.
Moving from a technical to a leadership role
Data science is all the rage, but some companies focus on hiring just data scientists. Be careful.
Let's walk through the decision of whether your company would benefit from building a Hadoop cluster.
Tech noncompetes can raise thorny issues. We offer some ideas to smooth things out.
use your customer data to really engage your customers
a look back at ORD Camp 2014
Similarities between music producers and consultants
Business Models for the Data Economy
announcing a new service: R training