APX Ventures is a company specializing in cryptocurrencies and digital assets and it will launch a series of products aimed to improve the cryptocurrency and blockchain space. APX tokens will run on a buy-back program which will use funds from the company to buy back and burn tokens.
|Mkt.Cap||$ 1.74 M||Volume 24H||0.00000000 APX|
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Companies must notify the ASX of any change in director within 10 business days. APX has developed an interactive asset registration process to allow for various requirements across markets to reside within one registry.
Nevertheless, Appen shares is expected to ride the next tide of big data and realize high long-term growth. Appen shares is highly exposed to, or even reliant on the development of the AI and machine learning market. The growth in AI and machine learning market, represented by the increase in capital investment and ML application, is predominantly enabled by the growth in computing power and the decline in storage cost. Potential threats also exist in synthetic training data and transfer learning, which would reduce the need for training data, which is the revenue source of Appen.
Content Relevance provides the annotated data for search and social media, while Language Resources provides speech data. The company essentially offers a cent or two per tag to more than a million crowd-sourced, low-paid and home-based workers in 130 countries but generally throughout Asia. Since 2007 one of the big problems in machine learning has been access to the training data needed to be fed into the machines. And since about 2013 there has been an explosion of image labeling requests. In order for a search engine to deliver an image of a shoe when you ask for it, the robot needs to know what a shoe is, and to recognize a shoe, the machine needs to be taught.
Appen (ASX APX) is a workforce management company with high exposure to the technology sector. Through crowdsourcing, Appen develops human-annotated datasets for machine learning and artificial intelligence.
Content relevance data train algorithms to give users more relevant results, such as eCommerce site search, web search and social media feeds. Language resource data trains algorithms to build virtual assistants, smart home devices, call centre systems and in-car infotainment systems. Such data processing jobs done by Appen’s crowds are usually laborious, repetitive and non-innovative, such as data/query evaluation, annotation, transcription and translation.
The way to teach a machine it to feed it data sets that have the images with shoes differentiated with tags. A simple way to monitor all your investments in one easy place such as ASX shares, dividends, funds & more. Our team of analysts provide weekly insights & analysis into undervalued ASX shares to help you build a market beating portfolio. All data on this page is supplied by the ASX, Morningstar and Market Index. The Top 20 Shareholders of APX hold 71.69% of shares on issue.
The balance sheet reflects the low-asset and high-labour business model, which is further demonstrated in its profit decomposition. As illustrated, that data collection (fees paid to external workers) and employee benefits (salaries paid to internal employees) takes up 64% and 13% of total revenue, respectively. Due to the nature of Appen’s business, tangibles/fixed assets are expected to be low. As illustrated, after the accounting adjustment of FY18, the percentage of goodwill of total asset went down from 60% to 37.6%. This is because part of the goodwill is recategorized as customer relationship, which takes up 15.8% of total asset in FY18.
‘machine learning or AI or artificial intelligence’ and ‘data’ were added to screening criteria to reflect the job done by Appen (data preparation) and the industry it is exposed to (machine learning). With more data, capital investments and application in the field of AI and ML starts to flourish. According to International Data Corporation, spending on AI and machine learning is expected to grow from $12B in FY17 to $57.6B in FY21. Deloitte Global predicts that the number of machine learning pilots and implementation will double in 2018 compared to 2017, and double again by 2020.
As capital investment in machine learning application increases, which is driven by higher computer power and lower storage cost, there is much more demand for annotated data and thus more business for Appen. Appen’s key advantage lies in its ability to collect and annotate data at a low cost, but it also faces problems, such as customer concentration and low fixed asset.
Therefore, any loss of key customer or project, or wrong timing of projects, could significantly disrupt the earnings report. In summary, the concentration of customer/project in the US has led to high currency risk, customer risk and low predictability in revenue. Appen (ASX APX) is a global leader in developing high-quality, human-annotated datasets for machine learning and artificial intelligence.
- Additionally, capital investment remains low, averaging less than 3M FY15-18 and no major platform investment is expected in the short term.
- And despite having a strong position in the markets it serves, the core Appen business model appears to have commodity-like elements.
- However, this advantage could vanish in the face of much lower data demand due to synthetic data generation as well as data process automation.
- The imbalance between the number of internet user and corresponding language content in developing countries, especially China and India, could be a massive accelerator for Appen shares’ profit.
- This may occur when the approximation is dependent on the value of numbers within the problem instance; these numbers may be expressed in space logarithmic in their value, hence the exponential factor.
Directors & Management
An investment in Appen is pitched as investing in high tech and the continued growth of Artificial Intelligence and machine learning across a spectrum of applications. At this point however these applications are dominated by social media ad and newsfeed relevance, and search relevance. As illustrated, Appen shares produce the highest revenue and one-year EBITDA growth among its selected peers, at 86% and 102%. Appen has a “crowd” of more than one million, which spans more than 130 countries and covers more than 180 languages.
Appen’s core advantage lies in its ability to provide low-cost labelled data by mobilizing a large pool of flexible workers. However, this advantage could vanish in the face of much lower data demand due to synthetic data generation as well as data process automation. Nevertheless, these technologies are yet able to handle language-related field, which is Appen’s core business, and they should not pose significant risks to Appen in the short run. Synthetic Training data & Transfer Learning.
Over the last three years, Appen shares outperformed S&P/ASX 200 Information Technology Sector Index and S&P 500 Information Technology Index by over 750%. APX reported average revenue growth of 40% FY15-17, and revenue grew more than 100% in FY18.
The relationship between Appen and its technology clients (Google, for example) is similar to that between a junior cook and a real chef. Junior cook deals with food, who chops vegetables, meat into eatable pieces, just like Appen prepares the data for Google to process. Then the chef (Google) puts magic (algorithms) to tomatoes, onions, spice and herbs to turn them into cuisines (better products). Appen is one of the top shares to buy for 2019. It’s really a low-tech business feeding the machines of high-tech customers like Google, Facebook and Microsoft with datasets for their algorithms to learn from.
However, new technology emerges to reduce the need for training data and thus the revenue of Appen, including synthetic training data and transfer learning. Appen is viewed as a workforce management company with high exposure to the technology sector. Appen finds “curated” workers; assigns them with the data processing projects and assures the data quality before sending the processed data to its clients, who are mostly technology leaders like Google, Apple and Microsoft. There is little doubt that huge investment dollars are being ploughed into the opportunity by big-tech companies so the need for greater volumes of higher quality training data is a trend that appears somewhat reliable.
There also exist problems that are exp-APX-complete, where the approximation ratio is exponential in the input size. This may occur when the approximation is dependent on the value of numbers within the problem instance; these numbers may be expressed in space logarithmic in their value, hence the exponential factor.
Its EBITDA grew 239% FY14-17, from 7.7M to 26.1M FY14-17. Language Resources revenue went up by 4%, impacted by a mix of work. Content relevance revenue up 146% and margin expansion to 21.7%, mostly driven by Leapforce acquisition. When examining most broker reports covering Appen it appears their growth assumptions are merely an extrapolation of recent compounded average annual growth rates.