Top 5 Challenges for Machine Learning Projects

Entrepreneurs, designers, and managers tend to overestimate the present capabilities of machine learning. They expect the algorithms to learn quickly and deliver precise predictions to complex queries. They expect wizardry.

Commercial use of machine learning, especially deep learning methods, is relatively new. They require vast sets of properly organized and prepared data to provide accurate answers to the questions we want to ask them. A business working on a practical machine learning application needs to invest time, resources, and take substantial risks.

A typical artificial neural network has millions of parameters; some can have hundreds of millions. A training set usually consists of tens of thousands of records. While a network is capable of remembering the training set and giving answers with 100 percent accuracy, it may prove completely useless when given new data. It is just one of the limits to current deep learning algorithms. Read on top 5 challenges for machine learning projects.

The black box problem

The early stages of machine learning belonged to relatively simple, shallow methods. For example, a decision tree algorithm acted strictly according to the rules its supervisors taught it: "if something is oval and green, there's a probability P it's a cucumber." These models weren't very good at identifying a cucumber in a picture, but at least everyone knew how they work.

Deep Learning algorithms are different. They build a hierarchical representation of data - layers that allow them to create their own understanding. After analyzing large sets of data, neural networks can learn how to recognize cucumbers with astounding accuracy. The problem is that their supervisors - the machine learning engineers or data scientists - don't know exactly how they do it. The problem is called a black box.

Artificial Intelligence supervisors understand the input (the data that the algorithm analyses) and the output (the decision it makes). While the engineers are able to understand how a single prediction was made, it is very difficult to understand how the whole model works.

Some AI researchers, agree with Google's Ali Rahimi, who claims that machine learning has recently become a new form of "alchemy", and the entire field has become a black box. It is a significant obstacle in the development of other AI applications like medicine, driverless cars, or automatic assessment of credit rating. What if an algorithm’s diagnosis is wrong? How will a car manufacturer explain the behavior of the autopilot when a fatal accident happens? How will a bank answer a customer’s complaint?

The black box is an issue in machine learning for in-app recommendation services. It turns out that web application users feel more comfortable when they know more or less how the automatic suggestions work. That is why many big data companies, like Netflix, reveal some of their trade secrets.

The research shows artificial intelligence usually causes fear and other negative emotions in people. People are afraid of an object looking and behaving "almost like a human." The phenomenon is called "uncanny valley".

We accept machines that act like machines, but not the ones that do the human stuff, like talking, smiling, singing or painting. Of course, this may change with time, as new generations grow up in a digital environment, where they interact with robots and algorithms.

Talent deficit

Although many people are attracted to the machine learning industry, there are still very few specialists that can develop this technology and that is currently one of common machine learning problems. A good data scientist who understands machine learning hardly ever has sufficient knowledge of software engineering.

The problem is drastic. The Chinese tech giant Tencent estimated at the end of 2017 that there were just about 300,000 researchers and practitioners dealing with AI worldwide. Element AI, nn independent company, estimates that "fewer than 10,000 people have the skills necessary to tackle serious artificial intelligence research". Machine learning engineers and data scientists are top priority recruits for the most prominent players such as Google, Amazon, Microsoft, or Facebook.

It makes salaries in artificial intelligence field skyrocket, but also makes the average quality of specialists available on the market plummet. I wrote about general tech brain drain before. With machine learning, the problem seems to be much worse.

According to NYT in the US, people with just a few years of experience in artificial intelligence projects earned in up to $500,000 per year in 2017, while the best will get as much as NBA superstars. In a court filing in 2016, Google revealed that one of the leaders of its self-driving-car division earned $120 million in incentives before he left for Google's competitor - Uber.