Blog
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Explainable AI & adherence to impending global government AI regulations
- May 5, 2020
When looking at the forecasted revenues of the AI market worldwide from 2018 to 2025 we see from $10 billion in 2018 to $126 billion in 2025. The business upside is significant, however with potential growth of this magnitude, risk and government regulation was inevitable. Governments around the world today see AI as a great enabler to […]
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Explainable AI is needed now; the time for experiments has gone
- April 28, 2020
Now, more than ever, AI projects need to deliver results quickly. As CMSWire notes, the times of long drawn out proof of concepts and AI experiments are gone; AI needs to deliver business results quickly. Looking at this in the context of Explainable AI this is critical. Explainability is not a nice to have, it […]
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Addressing bias in AI needs Explainability; you can’t fix what you can’t see
- April 14, 2020
Contemporary machine learning systems are different from traditional rules-based systems. With these traditional systems a series of rules were written that matched up with the desired operation of the system. In machine learning, the system learns how to make decisions from the data that is presented to it. Whilst this has many advantages, a very […]
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Enterprise XAI: Ensuring success within an Enterprise environment
- March 31, 2020
Explainability within Enterprise AI is critical, whether this is to comply with regulation such as the GDPR, for auditing your AI systems, for feeding back to customers, for getting buy-in from internal teams and boardrooms or actioning on the decisions made by the AI. This message is becoming very apparent, but how does this high-level […]
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AI Validation & Risk Assessment
- March 12, 2020
AI industry analysts state that AI projects and their real-world success metric is less than 8%. This is a staggeringly low number given the billions of investment and big bets being made. Unlike other traditional software implementations, AI has always been vague and it has been accepted that machines are correct in making their decisions. […]
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Containerized, Model Agnostic Explainable AI
- January 27, 2020
Chatterbox Labs’ Explainable AI software works with any AI model in any AI system, which our enterprise customers use for: Validating continual AI model business relevance Auditing, tracing & explaining any AI model (text, image and mixed data) Exploiting & reinforcing existing AI assets Complying with global government AI regulation initiatives Conforming to a unified […]
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Explainable AI (XAI) – Auditing & Measuring AI Investments
- November 1, 2019
With the plethora of AI Clouds, engines and platforms available to the enterprise everyone has an opinion on which is best to strategically lock into. AutoML still appears to still be the flavour of the day, however Chatterbox Labs see a different dimension to AI. Explainable AI by 2023 will be pivotal to any enterprise […]
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Tech Blog: Deploying Explainable AI APIs with existing AI assets & platforms
- September 27, 2019
Chatterbox Labs’ patented Explainable AI (XAI) product can explain any AI model. This is critical because it means that, rather than replacing all of your years of AI investments with a new explainable model (that may not achieve the performance that the existing system can), our XAI works hand in glove with your existing system […]
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Explainable AI Product APIs
- September 16, 2019
Chatterbox Labs has been working towards solving XAI for numerous years. Many companies are claiming to be making breakthroughs in the area of XAI, however, our view is that unless you’ve excellent academic credentials, world class research scientists & world class product engineers, teams resort to infusing a single academic algorithm into a product offering […]
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The path to successful Enterprise AI is littered with hurdles
- March 27, 2019
Building intelligent AI solutions from the ground up is hard. There are some excellent, and deeply complex, tools out there which could, in the right circumstances be used to build excellent solutions. These tools rely on many building blocks but there are two that are critical: Good quality data to fuel them. Typically, this means […]
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