Tag: machine learning

I used to watch science fiction movies and imagine we’d have these incredible machines that could interact with humans in amazing ways. I always believed more in the helpful and useful AI in Star Wars than the dangerous systems of movies like The Terminator or The Matrix. I could even imagine a time when we have sentient systems, though I’m not sure they are coming anytime soon.

I used to assume the Asimov’s Laws of Robotics would be implemented without any issue. After all, doesn’t everyone worry about security? These days I’m more cynical, and I wonder if anyone would actually bother to program limitations in a system. I expect more people would assume we’ll add in the laws later, once we know the other features are working.

I do think we will get more and more systems like IBM’s Watson, that are designed to handle many tasks. When I read pieces like this one from HBR, I actually get a little worried. Not because machines will destroy humans, but they will greatly impact and change our society, potentially in disruptive ways for society. While we’ve seen issues with manufacturing over the years, there are still humans involved, and we have learned to find other jobs over time.

The AI revolution, if that’s what’s happening, will occur quicker, and if the HBR piece is correct, will impact many more people, across a shorter period of time. Our economies and workforce may not adapt as quickly, which has a variety of economic implications for all of us, even those of us that might be secure in our positions. If we can teach systems to accomplish tasks at a level even close to what the average human can do, we might find ourselves either out of a job or working with some sort of robot.

I don’t know how quickly we’ll advance, or even how far we will go, but I do find that the leverage offered by implementing advanced technology is certainly increasing in a way that worries me.

Steve Jones

The Voice of the DBA Podcast

Listen to the MP3 Audio ( 3.3MB) podcast or subscribe to the feed at iTunes and Libsyn.

The world is changing quickly, and it’s becoming incredibly personalized thanks to digital technologies. I saw a fascinating video from GE on digital twins. These are digital representations of their products, using specific data (sensor, visual, weather, settings, etc.) to build a model of a specific piece of equipment. This model runs on a platform and constantly analyzes new data to evaluate and predict the performance of the system. With equipment like jet engines, power plants, and more, a tiny increase in efficiency can translate into incredible cost savings or revenue increases.

The idea of using digital twins is taking hold in other fields, and I expect that we’ll continue to see all sorts of digital representations and models of real world items. We are even starting to see this in medicine, with personalized treatments for various diseases, including cancer. By using more data, and powerful computing capabilities, we can tailor our treatment to the individual and their particular ailment.

This personalization has an annoying side as well, after all many of us have experienced more targeted advertisements and annoying uses of our personal information, however much of that is crude, and lacking in sophistication. Perhaps if the models using data about me would be less annoying if companies didn’t try to sell me a laptop a week after I’ve purchased one, or show me sales on products when I’m looking at SQL Server articles.

I’m amazed and hopeful that our computing systems will evolve, with more talented data scientists blending their knowledge in some problem domain with powerful computing capabilities and lots of data. I expect that some of the challenges we face with our aging infrastructures and physical systems will be helped by extensive data and powerful machine learning models specific to an instance.

Steve Jones

The Voice of the DBA Podcast

Listen to the MP3 Audio ( 3.2MB) podcast or subscribe to the feed at iTunes and Libsyn.

It’s interesting to look at the data business and how companies view DBAs, database developers, BI developers, data scientists, and more. There are some companies that really see value in our services, and I’m grateful for that. I’ve been gainfully employed for well over two decades to work with data. There isn’t much standardization in our jobs or what we’re expected to do, but I’ve grown comfortable with that. That’s one of the reasons the people working with me, my coworkers, are more important than the work. The work is the work.

As our systems become more advanced, there is concern over how much less some of our skills might be needed with new AI type systems. Will the move to smarter systems mean there will be more opportunity for us? Or less? Certainly in the long term there might be less jobs if systems become really capable, but in the short term I don’t think so. As much as Microsoft has improved SQL Server, and they’ve done great things with easier HA configs, the Query Store, Adaptive Query Processing (coming) and more, they aren’t replacing many of us. Maybe a few, but I think there’s still lots of technical work.

Darmesh Shah wrote a nice piece on AI and how it will help many of us in our jobs, providing the easy information and guidance for us to focus our skills. He sees bots as helpers, which to me presents new opportunities to interact with and work with customers and data. We will find ourselves more capable with help from AI, not replaced. As we get better machine learning or other adaptive algorithms, we’ll actually find new ways to work with data. This should provide us with new opportunities and new types of jobs that we might grow into. Plenty of people want to dismiss the data science jobs as a popular area where anyone can claim those skills if they know a statistical function and can query data in R, but there are real jobs in those areas, and there are opportunities in new companies that might not have existed in years past.

There are going to be some amazing new ways that data and more intelligent algorithms will help us see our world in the future. There will also be scary ones, and many we don’t know if we can trust. Somewhere in there, the world will become very, very interesting for those of us that work with data on a daily basis, looking for new ways to extract information from all the bits and bytes that we store. Once again, this is something I do look forward to as a data professional.

Steve Jones

The Voice of the DBA Podcast

Listen to the MP3 Audio ( 4.6MB) podcast or subscribe to the feed at iTunes and Libsyn.

I was reading about the Microsoft Cognitive Services and their wider release in preview to more developers. There are a few of the many machine learning services that anyone can use to build more intelligence into their applications. The entire Cognitive Services include some interesting sets of APIs that allow us to build new features and even new capabilities that we might never have considered without the power of machine learning to change the behavior of systems over time.

With machine learning, we have more and more ways to analyze data, allowing our systems to actually react to the data and become better at their particular task. I think there are certainly some dangers in how these systems might be hacked, but for many uses, I think ways that these platforms can work with speech, images and more might really alter the way in which we decide to build new applications in the future. The goal, at least according to Microsoft, is to have the AI services help humans accomplish tasks, not replace them. That’s something I hope actually comes true.

I do think that we don’t quite know how these machine learning systems will grow and interact with people over time. They work by analyzing data, and altering behavior based on data, which means that we need to better understand the implications and effects of various algorithms and systems. I hope that more companies and developers spend time experimenting and working with various APIs and services to learn more about them. I’d like to see more projects and proof-of-concept systems.

Some of us will just build things that aren’t useful, or that don’t even work well. That’s OK. We need to experiment, and understand these are experiments. These aren’t guaranteed ways of producing information from data. As long as we get that, or at least understand some of the work we may do for clients will need to be thrown away, that’s fine. I know there’s pressure in many companies to be efficient and just work on things that help the organization move forward. I’m sure some companies will make bad decisions, or even abuse their AI systems. I’m also hopeful that more will realize that some experimentation is necessary if you want to find new ways to create richer, more reactive and customized systems based on the massive amounts of data we collect every day.

Steve Jones

The Voice of the DBA Podcast

Listen to the MP3 Audio ( 4.0MB) podcast or subscribe to the feed at iTunes and Libsyn.