Research Publication – Artificial Intelligence – Pandora’s bots – Radio Free Mobile

May 12th 2023: RFM deepens its coverage of Artificial Intelligence with the publication of Artificial Intelligence – Pandora’s bots. 

RFM research subscribers will receive their copy by email. 

The 4^th hype cycle is in full swing but large language models (LLMs) do have a lot to offer. They won’t deliver superhuman intelligence, but they can greatly enhance the productivity of humans as well as their ability to use language as a user interface. These systems are very expensive to develop which is probably a major reason why it is the large companies like Microsoft (OpenAI) and Google that currently lead the race. The AI ecosystem is at a very early stage and remains dominated by Nvidia but for how long remains to be seen.

• 4^th hype cycle. Generative AI has for the 4^th time in 60 years triggered dreams of super-intelligent machines being just around the corner. Generative AI offers a big step forward in terms of ingesting, parsing and retrieving data but it continues to suffer from problems that are very unlikely to be eradicated in the foreseeable future. Hence when superintelligence fails to materialise, there is going to be another round of despondency and depression.
• No breakthrough. The techniques to create generative AI are not new and mostly stem from Google’s 2017 transformer neural network architecture. In 2012, deep learning suddenly started to work thanks to enough data and processing power becoming available. Generative AI looks like a repeat of 2012 rather than a genuine breakthrough that will change the world.
• Use case 1: Enterprise. RFM’s testing reveals that LLMs are extremely good at ingesting huge amounts of data and being able to usefully collate and amalgamate data to produce useful and easy-to-use answers to inquiries. This has substantial use cases within the enterprise and may even render some current corporate data solutions obsolete.
• Use case 2: Automotive: where the current smartphone-like user interface is woefully inadequate. Voice has always been the leading contender but has not been anything like good enough. Generative AI could substantially improve the ability to control vehicles with voice and offers a way back for OEMs to reclaim some of the ground that they have already ceded.
• Generative AI is expensive but right now no one seems to care. RFM finds that for services with millions of users, the cost of inference will be far greater than the cost of training. This creates a substantial advantage in running inference in end devices rather than in the cloud.
• Generative AI players: Despite its recent gaffes, Google is a leader in the generative AI space alongside Microsoft (OpenAI) having created a lot of the innovation that everyone is now using. Meta Platforms, Baidu and Midjouney make up 2^nd place with others like Alibaba, Amazon and SoundHound nipping at their heels. Apple & Samsung are nowhere to be seen.
• The AI ecosystem is at a very early stage and is currently dominated by Nvidia which has 85%+ share of the platform used to train AIs. However, the popularity of ChatGPT could move the control point away from Nvidia and greatly weaken its position.