Why Machine Learning is Good Business
Albert Bielinko, Growth Equity Investor, Telstra Ventures
Albert Bielinko, Growth Equity Investor, Telstra Ventures
MOVUS, for example, provides sensors that magnetically attach to rotating machines like pumps and collect information including temperature, vibration and sound, establishing a baseline for a healthy state. Machine learning is then applied to predict machine failure before it occurs. This use case can have significant return on investment and will only improve over time. Increasingly, the benefits of machine learning will be at the edge, reducing the need for data to be sent to the cloud. Video cameras are another great sensor input for machine learning algorithms. UBTech produces programmable intelligent humanoid robots that can engage with humans for use cases such as entertainment (they do push ups!), education and customer service. It incorporates speech recognition, natural language processing and facial recognition. The continual improvement of hardware is also enabling flexible robots who can react to their surroundings.
In this new paradigm, clean data becomes essential. Trifacta is the leader for data preparation and wrangling across any cloud, working with 12,000 customers like Pepsico and New York Life. Trifacta combines and cleans data sourced from disparate sources to allow tools like DataRobot to test different models and identify the most predictive. Automated machine learning toolkits for developers are making it simpler to plug and play harness the power of machine learning.
Other commercial applications include predicting failure in carriers’ mobile networks. HeadSpin enables companies like Mozilla and Telstra to test and manage their mobile apps on any network in any country in real time on physical devices. This provides a huge matrixed data set tracked in real time and correlated across a data lake that has been collected by carriers and apps.
Machine learning is also powerful in healthcare, where instead of needing an expert to consider a health problem, an expert could train a model to enable fast and cheap diagnoses at scale. The opportunities are enormous, however, issues such as avoiding biases, will need to be considered.
Albert Bielinko is a growth equity investor at Telstra Ventures. Telstra Ventures has invested $400M in 62 technology companies, including those named above.
