Why is anyone sorry to see the back of this guy? They won't be...

Why is anyone sorry to see the back of this guy? They won't be ulitimately if BRN can secure the services of someone better

But obvious concerns come in 2 guises.

1) As others have said, the natural suspicion, given his voluntary departure and relatively short tenure, is that having had time to assess the company in detail he may feel its future is rather less rosy than some people here like to imagine. There are other explanations, but there is a distinct possibility that this the correct one.

2) His departure risks allowing the obsessive greed of the old board to resurface unchecked, with a massive allocation of unearned freebies which, among other things, will insulate them from the effects of any large CLN accompanying the next fund raising. We all hope there won't be one but ....

Meanwhile here is an FT article, mainly highlighting the speed of change in competing traditional ML, may be subscription only. This doesn't mean there is no opportunity for SNNs, but it something to bear in mind which rarely gets discussed here.

Hardware revolution pushes AI into the mainstream
Some extracts

“Instead, the most eye-catching improvements have been coming from hardware. These rest on the specialised chips designed to handle the huge amounts of data required for machine learning, along with purpose-built systems tuned for the job. US research group OpenAI points to a hardware inflection point in 2012. "

"Before that time, Moore’s Law, the chip industry rule of thumb that processing power doubles every two years, ruled in the world of AI computing. Since then, AI systems have followed a Moore’s Law on steroids. With new types of hardware and greater resources being thrown at the problem, the capabilities of the most advanced AI systems have doubled every 3.4 months. There is a paradox in this hardware acceleration. On the one hand, at the frontiers of the science, it has turned AI into an arms race in which few will be able to play.”

“According to Stanford’s DawnBench project, which provides a way to benchmark AI systems, the time taken to train a system on the widely-used ImageNet data set has fallen from three hours to only 88 seconds in the space of less than two years. This translates into massive price deflation, with the cost falling from $2,323 to $12.”

"The other effect of the hardware revolution, however, has been to push the technology into the mainstream. Google’s TPUs, among the world’s most advanced chips for handling machine learning, can be rented by the hour through the company’s cloud computing platform (only $1.35, if your workload is not time-sensitive and you don’t mind being at the back of the queue)."