The theory behind Machine Learning has been developed and discussed for decades; what has been the hurdle for Artificial Intelligence (AI) until recently was the inability to deploy tremendous computing power in a cost-effective way, to solve complex problems.
The inflection point of change was reached around 2010 when AI researchers started using Graphic Processing Units (GPU) for machine Learning. In 2015 Microsoft ResNet beat the best human score in a Computer Image Recognition challenge, with an error rate of about 3.6% (the human error rate is around 5%).
For humans, about 80% of our information is derived by vision, more than any other sense. As AI models can now understand visual information at a “human-like” level, computers can rely heavily on machine vision to process information and communicate it to users. Think about Alphabet. In 2020 it introduced Google Lens that uses AI to make people understand what they see by pointing their phone camera on any subject, for example if you want to know exactly what species a flower is. Or think about Amazon, which introduced an AI image recognition tool that can identify a person, object or scene from an image or video, as well as analyzing face attributes.
Simply stated, computer vision is a fundamental component of AI, giving machines advanced processing ability that has dramatic commercial implications, impacting everything from how we drive to how we can shop for groceries.
The global Machine Learning market was valued at $1.58B in 2017 and is expected to reach $20.83B in 2024, growing at a CAGR of 44%. This technological revolution is also impacting the software application sectors, such as Cloud-based services, where revenues are expected to climb to $126.0B by 2025 from $10.1B in 2018 – Market Research.
The next step of AI innovation is to become more “human-like”. Advanced AI models aim to recognize people's emotions, attitudes, and the context in which conversations take place. In 2020 Google introduced Meena, an end-to-end neural conversational model that learns to respond sensibly to questions. Its main objective is to reduce the human communication complexity by decoding context and information to formulate adequate responses.
Source: Alphabet
According to the Sensibleness and Specificity Average (SSA), which is the existing human metric for evaluating conversation quality, Meena is the most sensible chatbot and it is closing the gap with human performance as no other model did before.
In summary, we are still at the early stage of this technological revolution. AI technology represents a virtuous cycle of innovation due to its ability to support a broad range of different applications. Today, AI is surely the major opportunity for businesses from different sectors to solve complex problems and integrate the massive amount of data generated by users.
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