To foster the next level of accessibility and inclusion, it’s time to start investing our efforts into developing more sophisticated cognitive AI machines. Developing more sophisticated forms of cognitive AI is the key to expanding global accessibility and broadening the scope of inclusion.
In fact, we already see unprecedented language coverage. Flint Capital notes that recent research shows the number of machine translation language pairs has soared from 16,000 to about 100,000 in a single year. The firm points to Niutrans and Alibaba as top contributors with 88,000 and 20,000 language pairs.
On top of this, Flint Capital also notes that the global cognitive computing market is projected to surge to $72.26 billion by 2027. We already see huge gains with the rapid development of new AI tech that pushes the existing limits of voice synthesis and speech recognition.
From a financial and technological advancement standpoint, it’s clear why there’s such a push to further AI capabilities. But why focus on accessibility and inclusion?
Currently, there are about 1 billion disabled people in the world. Additionally, experts estimate that there are about 7,000 distinct languages in our global community. As we become more connected, the challenges of accessibility and inclusion grow too great for humans to handle alone.
Let’s look at a few inclusivity problems businesses face that advanced cognitive AI can improve.
1. Language barriers can hinder self-expression.
As we continue to move toward a global economy and the idea of a worldwide workforce persists, we have to address the limitations of shared language.
There are thousands of languages and dialects spoken by employees worldwide. Even if they all share a common language (such as English), there are countless instances when they cannot adequately express their ideas or opinions effectively because of a language difference.
According to experts, adopting advanced cognitive AI engines and deploying VSAs is the solution to this problem. These machines are extensively trained to specifically handle a niche set of translation tasks, so they offer more inclusive and expressive capabilities without human involvement.
Cognitive AI also facilitates greater accessibility for those with disabilities. For instance, it’s capable of speech-to-text services for those with hearing impairments. Conversely, it can also generate speech from text to assist the visually impaired.
In the future, we expect to see improved cognitive AI services that can more effectively perform translation of speech, text, and images into multiple languages and in a manner that serves a broader range of disabilities.
2. Generic MT can’t handle sensitive translations.
Imagine trying to use Google Translate to create patient charts in a healthcare setting. Not only would this inevitably lead to confusion and mistranslation, but it could unintentionally harm a patient. Basic machine translation engines don’t understand industry-specific terminology, and any small error can have a ripple effect on patient care.
The same goes for other sensitive industries like law, finance and government. Businesses must be cautious about which machine translation engine they choose because few can perform at a high level.
This is where customization of cognitive AI comes in. Companies that can create customized, highly accurate machine translation engines offer a massive competitive advantage.
3. Tone, intent and enriched experiences are limited for specific populations.
Many languages rely on tone, context and gesture to convey meaning. In Chinese, a slight change of intonation completely changes the meaning of a phrase, and in Spanish, the formality of your relationship dictates how you should speak to someone.
Basic translation services cannot accurately read tone or intent. This can cause communication blunders or even unintentional offenses. As an example, in Korean, honorifics are extremely important when addressing someone, so it’s critical for a translation service to understand the formality of tone so it can convey the proper meaning.
Another problem along these lines is the potential for gender bias. Many languages differentiate between masculine and feminine, but there’s not always enough context for AI to correctly discern whether it should return a masculine or feminine translation.
Intento research has found that generic NMT engines default to masculine translations at least 90 percent of the time. However, since so many corporate operations have moved online and now encompass global teams, work is being done to correct for these biases.
In a 2021 report, Intento saw an uptick in multilingual NLP ML models. This expands end-user deployment capabilities and allows developers and users to adapt AI to specific needs.
The Future is More Inclusive Than Ever Thanks to Cognitive AI.
Currently, there are still plenty of limitations on what machine translation and cognitive AI can do. It cannot yet interpret the tone of a poem or the emotion in a song.
However, it can learn how to provide better, more nuanced conversational capabilities, which is already changing how global brands do business.
Experts like Flint Capital point to how the data shows that the global cognitive computing market is more robust than ever before, and there are no signs of slowing. Intento notes that the number of MT engines has nearly doubled in the past year.
Smaller tech companies are working on the development of cognitive AI, but industry titans like Google, IBM and Microsoft are throwing their weight behind this problem as well. Because of this, we expect to see unparalleled growth and expansion in cognitive AI capabilities in the near future.
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