It is so heartening to see ANN (aka Artificial Neural Network) explained so beautifully via a story that doesn’t get mathematical or theoretical and yet, drives the essence of ANN quite well.
I especially loved the learning part – making mistakes and learning from them, feature extraction and all that. Do watch – quite a brilliant insights about artificial neural networks.
P.s. This animation is more for layman or people who just wish to have a good idea about ANN, not for experts. Though if you’re already working with ANN, CNN or RNN – it might put a smile on your face. 🙂
Among all the debate about AI involving visionaries and business legends, here is a wonderful discussion with one of my favourite AI experts (and teacher) – Andrew Ng and some of his more humane concerns, considerations. I am glad he acknowledges reluctance of re-learning (reskilling) as one of the bigger challenges. I believe this is going to be a large part of the counselors’ or therapists’ work in the next few years as more people become redundant in their organizations.
A must read for anyone curious about AI and its future –
“As an AI insider, having built and shipped a lot of AI products, I don’t see a clear path for AI to surpass human-level intelligence,” he said. “I think that job displacement is a huge problem, and the one that I wish we could focus on, rather than be distracted by these science fiction-ish, dystopian elements.”
“I’ve been in a lot of private conversations with AI leaders, or business leaders who are working on new AI products that will wipe out tens of thousands of jobs in a single company, maybe more across multiple companies,” Ng said. “And the interesting thing is that a lot of people whose jobs are squarely in the crosshairs of the technologies, a lot of people doing the jobs that are about to go away, they don’t understand AI, they don’t have the training to understand AI. And so a lot of people whose jobs are going to go away don’t know that they’re in the crosshairs.”
“I think one challenge that’s harder to get around is that if I am a master welder, and a lot of my identity is bound up in the respect I command as a master welder, needing to take on a new role where I’m now a novice, I think that’s emotionally challenging,” he told VentureBeat. “That’s actually a significant challenge we need to get through. For what it’s worth, once I was a master Basic programmer, and then I had to learn Python.”
A wonderful post with lot of real life case-studies from one of the influential investors in the Sillicon Valley – Andrew Chen!
The first and most well-studied root cause of the Bad Product Fallacy is from the theory of disruptive innovation. Many products can look like toys before they become successful. Just take Instagram as an example – it was just a photo filters app at the beginning, and is now one of the largest media properties in the world. Or personal computers, which was initially meant for hobbyists since they were underpowered and weren’t useful for business applications.
And this a wonderful example how McKinsey failed to estimate market for cellular phones.
In the early 1980s AT&T asked McKinsey to estimate how many cellular phones would be in use in the world at the turn of the century. The consultancy noted all the problems with the new devices—the handsets were absurdly heavy, the batteries kept running out, the coverage was patchy and the cost per minute was exorbitant—and concluded that the total market would be about 900,000. At the time this persuaded AT&T to pull out of the market, although it changed its mind later.
– The Economist, Oct 1999
But of course, mobile phones as a luxury was quickly fixed. By making the cost per minute cheap and fixing the other technical issues, the mobile phone has become the most ubiquitous computing device in the world.
Hindsight is 20/20!!!