Why Machine Learning is Finally Ready to Leave the Lab
In large, that’s because businesses are still suffering from a chronic shortage of raw data, and the skills to interpret it effectively.
This hasn’t stopped companies from making serious investments in AI and automation technologies. A of close to 4,000 IT leaders across 84 countries found that more companies are starting to invest in AI and automation technologies, and they’ve been helped in this task by the growth in available data and improvements in compute and learning models. What lessons can other businesses take from these advances to drive their own AI and ML initiatives?
In the last year we’ve seen important progress in the development of data sets, hardware and software tools, and a culture of sharing and openness through conferences and websites like arXiv. Novices and non-experts have also benefited from easy-to-use, open source libraries for machine learning.
These open source ML libraries have levelled the playing field and have made it possible for non-expert developers to build interesting applications. It’s little wonder, then, that more companies are seizing the opportunity to build ML and AI into their systems and products.
Models are only one side of the coin, however. Many of the models we rely on, including deep learning and reinforcement learning, are data hungry. Since they have the potential to scale to many, many users, the largest companies in the largest countries have an advantage over the rest of us. It’s the reason why we’re seeing so much cutting-edge research coming out of the large U.S. and Chinese companies.
In a sense, AI is providing a solution to its own challenge by enabling organisations to generate labelled data sets. By augmenting human labellers with machine learning tools, organisations can help their human workers scale, improve their accuracy, and make training data more affordable. In certain domains, new tools like generative adversarial networks (GANs) and simulation platforms are able to provide realistic synthetic data that can be used to train machine learning models.
Machine learning researchers are constantly exploring new algorithms. In the case of deep learning, this usually means trying new neural network architectures, refining parameters, or exploring new optimisation techniques. The challenge is that experiments can take a long time to complete. The cost of computation means researchers cannot casually run such long and complex experiments, even if they have the time.
Our industry is well aware of these issues.
