There is no free lunch in AI

In conversations with a friend from university I learned about the No Free Lunch Theorem and how it affects the state-of-the-art of machine learning and artificial intelligence development.

Put simply, the No Free Lunch Theorem (NFL) proves that if an algorithm is good at solving a specific type of problem then it pays for this success by being less successful at solving other classes of problems.

In this regard, Algorithms, AI Loops and Machine Learning solutions are like people; training to achieve mastery in one discipline doesn’t guarantee that same  person is a master in a related discipline without further training. However, unlike people, algorithm training might be a zero-sum game with further training likely to reduce the competency of a machine learning solution in an adjacent discipline. For example, while Google’s AlphaZero can be trained to beat world champions at chess and Go,  this was achieved using separate instances of the technology. A new rule set was created to win at chess rather than adapting the Go rule set. Knowing how to win at Go doesn’t guarantee being able to win at chess without retraining.

What does this mean for the development of AI? In my opinion while there are firms with early-mover advantage in the field, their viable AI solutions are in very deep domains that tend to be closed systems, e.g. board games, video games, and making calendar appointments. As the technology is developed, each new domain will require new effort, likely to lead to a high number of AI solutions/providers. So rather than an AI future dominated by corporate superpowers there will be many providers, each with a domain-distinct AI offerings.



Conagra Vs Pinnacle Foods

Reproduced Courtesy of Market Watch.

We dug in to this story using our grocery data sets. By tracking promotional sales up to, and after, the announcement we saw a 2% year-on-year decline in average price paid for Pinnacle Foods’ products. Following Conagra’s announcement, there was an immediate price hike that took average prices  6% higher in the following six months.  As per this article, our findings suggest a lack of due diligence was performed during the transaction.

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M&A Data Base & API, Complete!

We have just released version 1.2 of our UPC  to ISIN / Stock Ticker mapping API and data base.

The data set covers 1,500 brands in 424 categories spanning 50,000 individual products.  We have also made our data  point-in-time accurate, going back 10 years.  Submitting a UPC and a date to the API and it will return the ISIN number of the company that owns product at that point in time – provided the company was public. For private companies the API can return customizable values.

In time, we will provide access to the underlying data so that firms can analyze the characteristics of firms that are acquiring packaged-goods brands as well as those brands that get disposed of.