The current state of applied data science [Ben Lorica / O’Reilly Radar]
Key points from the article:
- Lack of training data remains the primary bottleneck in machine learning projects
- Think about features, not algorithms
- Data enrichment can potentially improve your existing models – this is sometimes overlooked, though this is often not considered as glamorous as model and algorithm development
- Role of machine learning engineer has recently emerged to streamline process of productionizing data science projects
- Model and algorithm development gets all the media coverage but this is usually not as pressing as developing good training data sets and productionizing data science projects
What this overview misses is the rise of the data product manager. You can’t do good data science without a thorough understanding of business and market requirements. A good data product manager will provide that, and direct the data science team towards useful projects. You can’t take good problems well-framed as a given; that’s one of the biggest challenges of data science.
Bad code isn’t technical debt, it’s an unhedged call option [Frances Lash]
Therefore, even if it is more expensive to do thing clean from the start, it would also be less risky. A messy system is full of unhedged calls that can be called upon at an unpredictable cost to the organization. Technical debt does not communicated the risk of writing sloppy or quick fixes into code – debt is something to be managed and just another tool. When talking about implausible delivery dates it may make more sense to talk about unhedged call options.
What artificial intelligence can and can’t do right now [Andrew Ng / Harvard Business Review]
If a typical person can do a mental task with less than one second of thought, we can probably automate it using AI either now or in the near future.
[David DiSalvo / Forbes]
As predicted, the volunteers rated the allegedly higher-priced wine as tasting better than the allegedly cheaper wine. The MRI scan showed that when those evaluations were made, two parts of the volunteers’ brains experienced greater activity: the medial pre-frontal cortex and the ventral striatum. That’s important because those two areas are especially involved in evaluating expectations and seeking rewards. When we see a higher price, our brain links the price to greater expectation of reward, which changes our perception – in this case, taste.
Why the AI hype train is already off the rails and why I’m over AI already [Dat Tran / Built to Adapt]
In those early years of big data, the outcome was always less than perfect. Most of the work ended up in powerpoint presentations without ever going into production because most teams simply did not have the right infrastructure or culture to maintain them.