What are the differences between AGI, AI and machine learning? Our guest today knows the answer to this question, and much more about each of these fascinating areas of tech today.
Alex Bates, CTO of MTell, joins us to share his experiences in the world of machine learning and artificial intelligence. You’ll hear us discuss the differences between artificial intelligence, artificial general intelligence, and machine learning along with what’s next in these areas. Listen in for that and more on today’s episode of CTO Studio.
In this episode, you’ll hear:
- What is process optimization?
- How did Alex acclimate to his first CTO role?
- What are the differences between AI and AGI?
- Is AGI already here?
- What was it like to be with Richard Branson on Neckar Island?
- And so much more!
We dive into the deep end immediately with a discussion on when machines will become sentient, and how will we know they have become sentient. It’s hard for us to know to truly know our fellow human beings are sentient, so how could we possibly know when a machine is?
Alex believes it boils down to how you define sentient. He thinks there are degrees of awareness of yourself as an agent and other agents with their own goals. He says it is a good question as far as humans: are we living in a simulation? I agree, no matter what I cannot get into another person’s mind. So how do I really know they are thinking? And because I cannot get into someone else’s mind nor into a machine’s algorithm so I may never know when that machine becomes sentient.
Alex says I could be in his simulation or he could be in mine or we could both in Elon Musk’s simulation (or anyone else’s for that matter). When Elon Musk says there is a .0001% chance we are NOT living in a simulation – that’s disturbing! He hopes AI will help us answer this question of whether or not we are living in a simulation, along with many other pressing issues.
The reason I bring this up is because of Alex’s work in machine learning. He has a machine learning background – with Intel he built machine learning solutions. I asked him to expand on his experiences.
Early on in his schooling he did DARPA-funded research on neural networks. And more recently with Intel he helped build a machine learning platform actually for machines. So it was designed to use machine learning to predict and prevent failure for heavy machinery industries like oil and gas and refineries. They would monitor all the sensory data coming off the machines, learn to prevent catastrophic failures and environmental oil spills.
Late in 2016 they were acquired by a company out of Boston called Aspen Tech. He stayed on for a two-year period to help integrate the company, and that time period is up in October of 2018. It’s been an amazing ride for him and now he’s looking forward to his next chapter.
He tells us Aspen Tech is the world leader in process optimization, they are big in process manufacturing and industrial industries.
Because they started this work before cloud-based machine learning solutions were available, I asked if they just rolled their own. Alex said they did. He was a key developer, and along with a lot of other developers they built their own from scratch. Early on it was before both big data and before the resurgence of data science and machine learning so everyone thought they were crazy, and he says they were crazy back then! But they got lucky and the industry changed. It became cheap to do big data then all the deep learning and platform breakthroughs happened so it worked out well for them.
The company in which they did this was built by Alex and his co-founder Paul. Alex became the CTO and together he and Paul built a team of 10 full-time employees and 10 contractors. They also had partners who did some of the service enablement so they were able to do a lot with a smaller head count.