Machine Learning, Forgetting and Ignoring

The focus in AI is all about Machine Learning – training larger models with more data and then showcasing how that model performs on tests. This is testing two main aspects of the model:

  1. Ability to recall relevant information.
  2. To organize and present the relevant information so that it resembles human generated content.

But no one is talking about the ability to forget information and to ignore information that is not relevant or out of date.

Reasons to Forget

Forgetting is a natural function of learning. It allows us to learn new knowledge, connect new knowledge with existing (reinforcement) and deal with the ever increasing flood of information as we grow older.

For Machine Learning models this is a critical requirement. This will allow models to keep learning without it taking more time and effort (energy) as the body of available knowledge grows. This will also allow models to build their knowledge in specific areas in an incremental way.

ChatGPT has a time horizon of September 2021 and has big gaps in its knowledge. Imagine being 1.5 years behind in today’s day and age [see Image 1].

Image 1: Gaps in ChatGPT.

Reasons to Ignore

Machine learning models need to learn to ignore. This is something humans do naturally, using the context of the task to direct our attention and ignore the noise. For example, doctors, lawyers, accountants need to focus on the latest available information in their field.

When we start to learn something new we take the same approach of focusing on specific items and ignoring everything else. Once we have mastered a topic we are able to understand why some items were ignored and what are the rules and risks of ignoring.

Current transformer models have an attention mechanism which does not tell us what to ignore and why. The ‘why’ part is very important because it adds a level of explainability to the output. For example the model can end up paying more attention to the facts that are incorrect or no longer relevant (e.g. treatments, laws, rules) because of larger presence in the training data. If it was able to describe why it ignored related but less repeated facts (or the opposite – ignore repeated facts in favor of unique ones – see Image 2 below) then we can build specific re-training regimes and knowledge on-boarding frameworks. This can be thought of as choosing between ‘exploration’ and ‘exploitation’ of facts.

Image 2: ChatGPT giving wrong information about Queen Elizabeth II, which is expected given its limitations (see Image 1), as she passed away in 2022.

Image 2: Incorrect response – Queen Elizabeth II passed away in 2022.

Image 3: Asking for references used and ChatGPT responding with a few valid references (all accessed on the day of writing the post as part of the request to ChatGPT). Surely, one of those links would be updated with the facts beyond 2021. Let us investigate the second link.

Image 4: Accessing the second link we find updated information about Queen Elizabeth II that she passed away in 2022. If I was not aware of this fact I might have taken the references at face value and used the output. ChatGPT should have ignored what it knew from its training in favor of newer information. But it was not able to do that.

Image 4: Information from one of the links provided as reference.

What Does It Mean For Generative AI?

For agile applications where LLMs and other generative models are ubiquitous we will need to allow these models to re-train as and when required. For that to happen we will need a mechanism for the model to forget and learn in a way that builds on what was learnt before. We will also need the model to learn to ignore information.

With ML Models we will also need a way of validating/confirming that the right items have been forgotten. This points to regular certification of generative models especially those being used in regulated verticals such as finance, insurance , healthcare and telecoms. This is similar to certification regimes in place for financial advisors, doctors etc.

Looking back at this article – it is simply amazing that we are talking about Generative AI in the same context as human cognition and reasoning!

Controlling Expenses

Money worries are back. In response to rising inflation in the UK, Bank of England has been forced to increase the base rate to 4.25%. That is expected to squeeze demand in two ways:

  1. Improving savings rate to encourage people to spend less and save more
  2. Increasing cost of borrowing – making mortgages more expensive

If you are worried about making money stretch till the end of the month then the first thing to do is: understand your spending.

The best way to understand your spending is to look at your sources of money. Generally, people have two sources:

  1. Income – what we earn
  2. Borrowing – what we borrow from one or more credit products

It is important to understand credit cards are not the only credit product out there. Anything that allows you to buy now and pay later is a credit product – even if they don’t charge interest.

Understand your Income and Expenses

Go to your bank account or credit-card app to understand how much money you get in each month, how much of it is spent and how much do you borrow over that amount. Most modern banking apps allow you to download transactions in comma separated values (csv) format or as an excel sheet. Many also have spending analytics that allow you to investigate the flow of money.

Once you have downloaded the data you just need to extract 4 items and start building your own expense tracker (see Excel sheet below).

Four pieces of information are important here:

  1. Date of Transaction
  2. Amount (with indication of money spent or earned)
  3. Categorization of the Transaction
  4. Description (optional) – to help categorize the transaction to allow you to filter

For the Categorization I like to keep it simple and just have three categories:

  1. Essential – this includes food, utilities (including broadband, mobile), transport costs, mortgage, insurance, essential childcare, small treats, monthly memberships. This is essential for your physical well-being (i.e. basic food, shelter, clothes, medicine) as well as mental and emotional well-being (i.e. entertainment, favorite food etc. as an occasional treat).
  2. Non-essential – this includes bigger purchases (> £20) such as dining out, more expensive entertainment, travel etc. that we can do without.
  3. Luxury – this includes purchases > £100 which we can do without.

The Excel sheet below will help you track your expenses. The highlighted items are things that you need to provide.

The items that you need to provide (highlighted in the sheet) are:

  1. Transaction data from your bank and credit-card (Columns A-D), this should be for at least 3 months – this is time consuming the first time as you will have to go through and mark each entry as category 1, 2 or 3 (see above). One you do it for historic data you can then maintain it with minimal effort for new data.
  2. Timespan of the data in months
  3. Income

This will generate the expenses in each category on a total and monthly basis (to help you budget).

This will also generate, based on the income provided, the savings you can target each month.

Note: All results are in terms of the currency used for Income and the Transaction records.

Things To Do

Once you understand what you are spending in the three categories you can start forecasting by taking monthly average spending in each category.

Tracking how monthly average changes over a few months will tell you the variance in your spending. Generally, spending rises and falls as the year progresses (e.g. rising sharply before festive periods like Christmas and Diwali and falling right after).

You can do advanced things like factor for impact of inflation on your monthly spending and savings.

Finally, once you have confidence in the output – you can move items between categories. This will allow you to play what-if and understand the impact of changing your spending patterns on your monthly expenses and savings.