What is Machine Learning?

Misconceptions about machine learning (ML) are running rampant among business leaders, and hindering organizations from effectively using and seeing results from transformative technologies. A new report from Forrester demystifies what ML really is and how to differentiate between complex terms. These are a few important takeaways you can use to get literate in machine learning and avoid the most harmful misconceptions.

Machine Learning is Misunderstood

Because there are so many complicated terms being used sometimes interchangeably or incorrectly – like neural networks, deep learning, and artificial intelligence – it can be very difficult to understand what machine learning really is. Forrester defines machine learning as “applied statistics and other algorithms to identify probabilistic relationships in data.” ML is not a number of things: it is not computers learning to think and make decisions like humans, nor is it all about predicting the future. In reality, ML is about identifying patterns in data. Depending on the context, and when the future resembles the past, machine learning can be helpful with predicting the future – but it’s not always prescriptive.

Best Tasks for Machine Learning 

ML is best used for ingesting and analyzing large amounts of data at scale and across multiple sources that are too complex for people to handle. However, ML struggles in situations where the data is noisy, limited, or when human judgment and reasoning is required. Optimal use cases for ML include: 

  • Classification: categorizing observations
  • Regression: predicting a continuous range
  • Clustering: sorting data into groups based on similarities
  • Anomaly detection: pinpointing exceptions
  • Association rules: if-then scenarios

Good ML models will be less biased than most people, but ML is only as objective as the data that people provide it. As a result, it’s important to be aware of the bias that may exist in your data and take steps to minimize it in order to avoid letting the technology inherit historical inequities.

Machine Learning is not a Black Box

A fear of incorporating machine learning into business processes often stems from the incorrect assumption that ML models are too complex and that the rationale behind its decisions can’t be explained. The reality is that ML is more transparent than people – it’s just harder to explain. According to Forrester, “People are excellent at explaining their decisions, but their explanations frequently have little to do with their actual decision-making processes. The reverse is true of ML models”. Explaining ML models requires high ML literacy, as well as clear communication from data scientists. If an organization lacks either of these aspects, it can be difficult to grasp the machine’s decisions. 

Propel Your Business Towards Growth

Incorporating AI and ML can propel businesses towards incredible growth – but it requires a deep understanding of each in order to succeed. Download Forrester’s report to learn the Seven Myths of Machine Learning and enhance your machine learning IQ. 

Why We’re Not Panicked About The Future Of Third-Party Cookies

Google announced recently that it will bolster privacy protection online by joining Safari and Firefox in blocking third-party cookies in its Chrome web browser by eventually phasing them out “within two years.” This is mostly relevant for retargeting and attribution outside of Google and Facebook’s platforms.

Unlike many ad tech solutions, Albert has never been reliant on third party cookies to deliver performance because the platform is built to combine ongoing analysis with execution.  For example, instead of being dependent on cookies for attribution, Albert uses continual lift testing to find the effect of campaigns on bottom-line revenue. This is a far more robust methodology than using cookies to track conversions. 

Albert is architected this way because the system is responsible for the allocation of budget. In order to do this well, it is critical Albert has the best data to understand at any moment what is working. This is the difference between a platform that actually controls execution and those that are designed to assist in campaign management or simply provide attribution reporting. 

While the next two years may bring uncertainty for much of the ad tech ecosystem, it does not for us. Albert is built on top of Google and Facebook and operates each publisher’s tools far beyond the capability of humans. We anticipate that Google and Facebook will develop and roll out new solutions within their platforms for tracking on the open web. As they do that, Albert will immediately be positioned to take advantage of them as a preferred tech partner of both publishers. Alternatively, if clients would rather use other new technologies for attribution or verification, the data will be able to flow into Albert in the same way we are able to receive it today.