‘I am he as you are he as you are me’
This is the first line of The Beatles’ song ‘I am the Walrus’, a song deliberately written to make no sense in order to satirise the over analysation of their other lyrics.
Predicting the predictable causes the predictable to become unpredictable
A sentence which, at face value, seems to be as incoherent as the previous one, but let me explain… Read More
Daily Archives: April 3, 2019
I spent 20 minutes trying to predict the stock market with AI — these are my results
This all started when I was asked to speak at an AI FinTech forum in July. It represented a great opportunity for me to talk about Machine Box to a new audience. But there was a problem. I don’t know anything about financial technology!
If you follow my posts, then you know that I frequently use predicting the stock market as a prime example of how not to use machine learning. The stock market is a highly complex, multi-dimensional monstrosity of complexity and interdependencies. Not a good use case to try machine learning on.
But… what if you could predict the stock market with machine learning? Read More
Israeli startup I Know First uses algorithms to choose investments with the highest returns.
Most fintech companies offer better interfaces for handling money (bank accounts, loans, payments, etc.), but quite a few startups are also offering a solution for a much older need than how to make money.
Israel company I Know First, managed by CEO Yaron Golgher, is one of these. The company’s main product is an algorithm that provides a forecast for three thousand different investment instruments, including shares, commodities, interest rates, foreign currency, exchange traded funds (ETFs), and global indices. Read More
A Radical AI Strategy – Platformication
A new business model strategy based around intermediary platforms powered by AI/ML is promising the most direct path to fastest growth, profitability, and competitive success. Adopting this new approach requires a deep change in mindset and is quite different from just adopting AI/ML to optimize your current operations.
As a data scientist you may be wondering why you need to be concerned about strategy and business models. It’s simple. Different types of AI/ML are most appropriate for different business objectives. So whether you’re a data scientist being asked to plan and present the most appropriate portfolio of projects, or a CXO looking to support your new digital business model, you need to understand the relationship between data science and strategy. Read More
Now that We’ve Got AI What do We do with It?
Whether you’re a data scientist building an implementation case to present to executives or a non-data scientist leader trying to figure this out there’s a need for a much broader framework of strategic thinking around how to capture the value of AI/ML.
Let’s start by just enumerating the broad categories of AI/ML business models. Most of us agree there are at least these four.
AI/ML Infrastructure
AI-First Full Stack Vertical Platforms
Applied AI – Optimization of the Current Business Model
Platformication – A Radical End Point for AI/ML Strategy
Read More
Microservices Anti-Patterns
Microservices is a silver bullet, magic pill, instant fix, and can’t-go-wrong solution to all of software’s problems. In fact, as soon you implement even the basics of microservices all of your dreams come true; you will triple productivity, reach your ideal weight, land your dream job, win the lottery 10 times, and be able to fly, clearly.
While this sounds like a lot of hyperbole wrapped up in some BS, if you have been listening to anything around microservices recently you will most likely have heard something not too far from this exaggerated sentiment — especially if it is coming from sales folks.
As a result of this, you or someone you know will likely have been charged by management to implement a solution in microservices or refactor an existing application to take advantage of microservices to ensure that you get all the magic. With so much overinflation of the truth out there, chances are you may have also implemented a microservices antipattern. These antipatterns are actually more common in the wild than fully functional microservices architectures. Read More
MIT Introduction to Deep Learning
MIT’s official motto is “Mens et Manus” — Mind and Hand — so it’s no coincidence that we, too, are big believers in this philosophy. As the organizers and lecturers for MIT’s Introduction to Deep Learning, we wanted to develop a course that focused on both the conceptual foundation and the practical skills it takes to understand and implement deep learning algorithms. And we’re beyond excited to share what we’ve put together with you, here and now: a series of nine technical lectures and three TensorFlow software labs, designed to be accessible to a variety of technical backgrounds, free and open to all. Read More
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