by twieberneit | Feb 19, 2024 | Blog, Sponsored |
During ZohoDay24, Zoho amongst other topics, gave some insight into how the company looks at AI. Raju Vegesna presented Zoho’s AI vision and progress. Additionally, I had the opportunity for a one on one with Zoho’s director of AI research, Ramprakash (Ram) Ramamoorthy. If you want to listen and watch the interview, you can do this here. Both represented a vision that is refreshingly differentiated from the current hype with everyone and their dog talking like large language models, LLMs, are the everything one needs. Well, let me tell you: They aren’t. But let me come to this point later. In addition to not every language model being created equal, and typical for a hype, there is still too much talk about the technology itself, whereas in the words of Raju and Ram the best AI implementation is “when the customer doesn’t know they are using AI but finds value in the output”. This resonates very well with me, as one of my beliefs is that the customer shouldn’t care about the technology that is used to achieve the desired outcome, within some constraints like legality, ethics, and efficiency, of course. Zoho is a technology vendor with a focus on business applications. So, Zoho quite quickly realized that consumer type AI that e.g., helps with spell checks, or nowadays research, suffers from two fundamental flaws: lacking privacy/security and accuracy when it comes to business applications. Both violate some of Zoho’s core tenets, namely their pursue of privacy and business applications that offer a lot of value to the customer. Take the example of improving one’s writing – for some...
by twieberneit | Feb 15, 2023 | Blog |
Now, that we are in the middle of – or hopefully closer to the end of – a general hype that was caused by Open AI’s ChatGPT, it is time to reemphasize on what is possible and what is not, what should be done and what not. It is time to look at business use cases that are beyond the hype and that can be tied to actual business outcomes and business value. This, especially, in the light of the probably most expensive demo ever, after Google Bard gave a factually wrong answer in its release demo. A factual error wiped more than $100bn US off Google’s valuation. I say this without any gloating. Still, this incident shows how high the stakes are when it comes to large language models, LLM. It also shows that businesses need to have a good and hard look at what problems they can meaningfully solve with their help. This includes quick wins as well as strategic solutions. From a business perspective, there are at least two dimensions to look at when assessing the usefulness of solutions that involve large language models, LLM. One dimension, of course, is the degree of language fluency the system is capable of. Conversational user interfaces, exposed by chatbots or voice bots and digital assistants, smart speakers, etc. are around for a while now. These systems are able to interpret the written or spoken word, and to respond accordingly. This response is either written/spoken or by initiating the action that was asked for. One of the main limitations of these more traditional conversational AI systems is that they are...