TECHNOLOGY
every time we tag a picture of someone on Facebook , for example , or on Instagram , we also use computer vision - it is applied everywhere around us .”
The main adopters and the challenges they face Borowska cites IBM Watson , which has built one of the most widely-known computer vision software available called Power AI . In healthcare , the software beats records in skin cancer detection . She adds that the Big Tech giants such as Google , Amazon , Facebook and Apple are leading the development and application of computer vision research .
Shaji adds Clarifai to the above list , but says that newer companies in the market are set to disrupt the space by making computer vision understandable and usable by anyone , regardless of position and / or job title . He adds they can offer on-premise solutions so they run locally on client systems , with no data being sent back to vendors , giving them total data privacy .
When it comes to associated challenges of adopting any new technology , Borowska says that systems are only as good as the data fed into them , similar to AI . “ If the data is not representative of the total population they analyse , we are likely to get a biased outcome . If you train a system to recognise a shoe , but only feed it pictures of trainers and boots , when a pair of heels comes along , it is unlikely to recognise it as a shoe . That ’ s why Apple ’ s facial recognition famously failed to work on black faces . Statistical analysis of the data is required to ensure all relevant cases are represented in the data input ,” she states .
According to Shaji there is also the issue of ‘ bottlenecks ’ and the need for expert AI scientists and technical support teams
Early
experiments in computer vision started in the 1950s and it was first put to use commercially to distinguish between typed and handwritten text by the 1970s . Today the applications for computer vision have grown exponentially .
By 2022 , the computer vision and hardware market is expected to reach $ 48.6 billion .
to both train and install solutions : “ Mass adoption of computer vision technology hasn ' t happened yet because most solutions still require expert AI scientists and technical support teams . Furthermore , huge amounts of data sets and heavy computation are required to train most machine learning models . Most of these are not user-friendly and incomprehensible to users beyond the technical field .”
Democratising the technology Shaji went on to say that vendors are now looking to democratise the technology so people can build their own applications .
82 December 2021