AI News, Machine Learning with Less Training Data – Approaches and Trends

Machine Learning with Less Training Data – Approaches and Trends

Expert systems and machine learning are two ends of a spectrum working to solve similar problems quite differently.

Expertise: Expert systems, machine learning, small business applications of AI Brief Recognition: Evan Prodromou is the founder of, a company which claims to allow businesses to build AI systems for price optimization, product recommendations, and lead scoring without the need for networks of data.

Small businesses can, however, use the knowledge they already have about their customers or their domain in order to come up with hypotheses they can test using a hybrid system that makes use of both logic and data.

An existing customer (one who has already purchased) might not get such a discount because trust with them has already been established, and their email details (for future promotions) has already been collected.

If it’s selling well, the system might actually determine that lowering the price for a potential first-time customer isn’t necessary because he or she is likely to purchase anyway.

Even also suggests that the transparency of a system like this is useful for small businesses because they won’t require IT experts to work within it and they can still garner useful insights for their business without potentially expensive, complex machine learning systems.

Evan suggests three actions businesses can take in the interim in order to prepare for it when it becomes accessible: Dan (3:30): How would logic work with regards to dynamic pricing?

We can encode things like “first time customers, you want to give a big discount to in order to get them to become long term repeat users.” Popular products you really don’t need to give a big discount on because they’re obviously selling well.

You can put together very straight forward rules based pricing algorithm with 10, maybe 20, rules that can be very simple and really reflect basic business information Dan (6:25): Could these 10 or 20 rules apply across an entire site or just particular products?

You can just look at a decision and say, “clearly the system was looking at the user’s age, and this is what came out, and this is why we made a decision.” In a machine learning model, that could be buried deep in a neural network, and so it’s hard to audit where the decisions happen.

Can we still understand the core premises at work and know what a decision is an why a decision is an at the same time leverage machine learning and have that actual learning happening in real time, not just routing through simple yes/no gate?

There is a wide array of different applications within there, and different companies are going to find what works for them Dan (15:35): One of the dynamics that your business is predicated on is that there will be a middle ground between unlimited data machine learning and ice cold yes or no systems that can solve a lot of problems.

We need to start looking at places in those software systems that we can start putting self optimizing intelligent features, whether its showing the price on your ecommerce site or whether, for an internal system, deciding about calendaring or figuring out where the best place to fit a meeting is.

Machine Learning

Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.

The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.

Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.

Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.

A practical guide to machine learning in business

But even as the technology advances, companies still struggle to take advantage of it, largely because they don’t understand how to strategically implement machine learning in service of business goals.

Instead of writing algorithms and rules that make decisions directly, or trying to program a computer to “be intelligent” using sets of rules, exceptions and filters, machine learning teaches computer systems to make decisions by learning from large data sets.

Machine learning is suitable for classification, which includes the ability to recognize text and objects in images and video, as well as finding associations in data or segmenting data into clusters (e.g., finding groups of customers).

for example, the latest version of CorelDRAW uses machine learning to interpolate the smooth stroke you’re trying to draw from multiple rough strokes you make with the pen tool.

Rule-based machine learning

Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves 'rules' to store, manipulate or apply.[1][2][3]

The defining characteristic of a rule-based machine learner is the identification and utilization of a set of relational rules that collectively represent the knowledge captured by the system.

This is because rule-based machine learning applies some form of learning algorithm to automatically identify useful rules, rather than a human needing to apply prior domain knowledge to manually construct rules and curate a rule set.

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