We are now in a national conversation that is no longer around whether, but how to reorganize cities to more effectively balance the power, load, and resources currently bestowed in police departments. Whether you’re an active part of the “defund” conversation, a police chief, or anyone in between, objective data should be a basis for this conversation and any conclusions. To that end, this post presents a preliminary, novel dataset that seeks to be a useful tool in that larger conversation: the budgets and populations of the largest 317 U.S …
Are you a startup seeking to prepare yourself for artificial intelligence (AI), but don’t yet have a concrete strategy in place? Perhaps you are a well-established data-centric organization that wants to assess return on investment and areas of strength and weakness. Or you are an investor looking for a way to quantitatively assess your portfolio. Catalyst Fund’s new AI Readiness Toolkit can help you.
Seemingly, everyone has something to say about the abundance of data, and the potential of AI to harness them. …
At BFA, we have been working on an R&D program called FIBR. We partner with local businesses in Ghana and Tanzania to explore the potential for burgeoning technologies, such as smartphones and machine learning, which are increasingly available in these markets, to create opportunities to expand access to financial services. A major component of this program is to quickly assess the fit of a potential partner/product/solution for a relatively lengthy engagement.
While one seemingly attractive approach to partner selection is to meticulously design an airtight system at the outset, we have found that the best means to understand a system…
Note: I tend to get a bit wordy when editors leave the room… So, dear reader, in an attempt to relieve you of any perceived obligation of reading this post from front to back, I am including some quick links to the subsequent sections here at the top:
1. My Motivation (for building a rig + writing about it)
2. Hardware Specs + Final Pricing
3. Assembling the Hardware
4. First Boot!
5. Setting up the OS + Drivers
6. Installing the Required Software
7. Configuring Remote Access
8. Running Some Deep Learning Benchmarks
9. High-Level Conclusions
10. What’s Next?
11. Useful Resources…
Machine Learning (ML) technology can help us draw important insights from data, but it is imperative to recognize a model is not an end in and of itself. Based on BFA’s experiences engaging with early-stage partners in emerging markets, such as Catalyst Fund investees, we have seen the consequences of rushing into machine learning without a clear understanding of the underlying data. As a business, misreading this data can cause you to chase errant hypotheses around the needs of your core set of customers, which in extreme cases, can cost you everything. …
The lending and credit scoring sector have more data than ever before at their disposal. How they leverage this data to create value for their clients and social impact determines the outcomes they can achieve in the financial services space.
In 1959, Arthur Samuel, a pioneer in the field of machine learning and artificial intelligence during an era when computers filled an entire building, defined Machine Learning (“ML”) as:
A field of study that gives computers the ability to learn without being explicitly programmed.
Open APIs can be crucial in getting a new product or set of features to market quickly and efficiently. There are well-known APIs such as Facebook and Google sign-in for authentication, APIs like Stripe and Paypal for payments, APIs for navigation including Waze and Google Maps, and many others for nearly every type of service imaginable. While these constitute a major presence in the most mature technology centers of the world such as Silicon Valley, we have seen that APIs prove just as critical if not more, in the developing world, and through the lens of innovation in inclusive fintech.
Inclusive Technologist • Data Scientist • Product Hacker