Appen: Leading the way in machine learning

The argument that eventually machines will render human input in the machine learning process redundant is inaccurate. Machines require vast amounts of training data to achieve competency in tasks that humans can process quickly and easily.

Appen CEO Mark Brayan and Figure 8’s VP of product management and design, Alyssa Simpson Rochwerger, hosted us at Figure 8’s San Francisco office. Figure 8 is a typically San Franciscan tech business, filled with bean bags, musical instruments, greenery, gaming consoles, casually dressed staff and even a couple of friendly canines roaming the floor. Despite the casual air, there was also a clear sense of enthusiasm, entrepreneurial spirit and an understanding of the significant role Figure 8 plays in the rapidly evolving field of machine learning and the artificial intelligence applications that derive from it.

One of the key messages from our time in the Figure 8 offices was that the quality of a machine learning algorithm is directly correlated with the quantity and quality of the training data used to develop it. 

Machines need human intervention to learn

Machines require vast amounts of training data to achieve competency in tasks that humans can process quickly and easily. Examples include using machines to perform visual inspection tasks such as scanning supermarket shelves for diminished inventory or spotting paint blemishes on an automotive production line. For a machine to perform visual inspection tasks, such as these with low error rates, the algorithms that drive them require exposure to the many situations they are likely to encounter, a lot of which will be subtle and nuanced – the so called ‘edge cases’ – that require high-quality human annotated data.

The opportunity set of real world problems with narrow and specific use cases requiring visual decisions is only limited by our imagination. To solve these problems at massive scale, whilst ensuring high quality and low cost, requires a combination of machine annotation and human input for the ‘edge cases’ that an algorithm has not yet been trained to identify. In combination, Appen and Figure 8 are uniquely positioned to solve many of these evolving machine learning challenges by drawing on the automation and speed of machines as well as the power of the human mind to quickly identify and tag the more nuanced cases.

The argument that eventually machines will render human input in the machine learning process redundant is inaccurate. Machines can deliver high levels of confidence in the predicted outcome for a given set of inputs in a majority of cases but to achieve the highest levels of confidence in all possible scenarios – returning again to the concept of the ‘edge case’ – requires huge amounts of high quality training data that a machine cannot generate on its own. Is 98% confidence in 98% of likely scenarios good enough? It is until the machine fails when faced with an edge case. Depending on the application, failure can result in catastrophic outcomes both in economic terms and with respect to human life (autonomous vehicles, for example). The potential for catastrophic failure sits at the core of underlying trust and transparency concerns with AI – and the only way to solve for it is through the application of large volumes of high quality training data.

Leadership in machine learning

Figure 8 is helping democratise the development of applications based on machine learning through its unique and scalable customer platform. Customers can self-serve, keeping costs low, as well as access a library of templates for sourcing training data. For applications that are amenable to automation – typically visual and some audio – machines can accelerate the data annotation process and for situations for which automated annotation is less applicable – content relevance, for example – customers can access the services of the human ‘crowd’. For smaller organisations, Figure 8 provides access to large-scale machine learning infrastructure at relatively low cost.

Appen is currently focused on three strategic investment areas:

- Annotation efficiency – Achieving the lowest possible unit cost whilst maintaining high levels of quality is a key focus for the business.

- Flexibility – Annotate more types and more complex data. Fostering a culture of agility that allows the company to adapt to new data types as they emerge.

- Quality – continual improvement. 

Consistent with Appen’s business flexibility focus is its evolving capability in the supply of data annotation services in highly secure environments, particularly for government clients. Appen currently runs two secure facilities and this will probably grow over time. Appen is also working on the development of a more secure ‘at home’ data annotation service.

A truly global addressable market

Appen is looking at broadening its business focus beyond the US with an initial focus on growing its existing operations in parts of Europe and Asia. Figure 8’s platform helps in these regions as it offers a lower price point. Longer term, China is the big prize however navigating the operational, cultural and political challenges China presents will be a significant task. Protecting the company’s intellectual property is paramount. In a country that has publicly stated its desire to be a global leader in the field of artificial intelligence the risk of IP theft or replication is presumably high. To this end, the Chinese operations will have their own, separate, technology stack. This will allow the Chinese team to go as hard as they can at developing their own capabilities without putting Appen’s core technology at risk.

The current anti-trust issues swirling around some of Appen’s largest customers in the US probably presents an opportunity for Appen as these customers seek to avoid the worst case outcome of corporate break-up. One of the issues the large US tech companies need to manage is how they use customer data. This is an area where Appen believes its services can be helpful, particularly their secure offerings, with respect to improving biased data models and in the development of better algorithms that deliver superior content delivery outcomes or through the proactive identification, management and removal of malicious content. 

Appen brings a far more rigorous approach to the development of the economic model within Figure 8. Appen brings an ‘obsession’ with unit economics to the broader group and should help Figure 8 successfully transition from start-up to a strong and profitable business. At the same time, Appen is conscious of fostering the entrepreneurial culture within Figure 8 and will also seek to leverage the inherent advantages this brings to the combined group. An example is driving a shift in the sales culture from ‘hunters’ to ‘farmers’ – that is, recognising and targeting the high incremental value derived from securing additional work from existing customers rather than the prevailing focus on securing new customers with all the associated customer acquisition costs and higher risk of churn. Other examples include avoiding very small customers for which the lifetime value to cost of acquisition ratio is unfavourable and encouraging the monetisation of the company’s professional services capabilities. 

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Mason Willoughby-Thomas
Portfolio Manager, Ausbil MicroCap Fund

Mason has over 15 years of financial services experience with companies including Australian Ethical Investment, ING Investment Management, ABN AMRO and AMP Capital Investors. For the past 5 years, he has been focused on small and microcap companies.

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