A decade ago, the phrase “computationally intelligent” was thrown around as a synonym for a job seeker.
But in today’s world, it means anything that has to do with artificial intelligence, or AI, or whatever it is you want to call it.
You might not even know what it means, but in the next decade, it’s likely to have a new meaning.
Computer science is a field of study that focuses on the development of computers, their algorithms, and the software that they use.
As such, it encompasses a lot of different areas, from programming languages to machine learning, to machine vision and other AI-related fields.
In this talk, I’ll walk you through some of the key areas that make up the field and discuss some of its more popular and challenging topics, as well as some of their more niche uses.
This talk will start with a brief overview of the field.
It then explores some of those areas in detail, and then dives into some of them in greater depth.
It will then talk about some of these areas, along with some of what the field can teach you about the future of computer science and artificial intelligence.
The talks will cover topics like machine learning and deep learning, as they relate to different kinds of problems, such as how to design better ways to solve them, how to optimize algorithms for different kinds, and more.
The speaker will also walk you step-by-step through a variety of approaches to applying computational thinking, including:How do we apply computational theory to practice?
What’s the difference between computational and cognitive thinking?
How do you use algorithms to make decisions?
How can we apply a computational theory approach to the development and evaluation of new technologies?
And moreWhat are the most pressing challenges facing the field of computer-aided design (CAID) today?
How might CAID fit into the future?
The slides are available here (PDF) for the rest of you to take a look at.
This one covers a lot more ground than you may have initially expected.
The first part of the talk will cover some of our top challenges facing this field today, and how we might solve them.
Then, the next section will cover the future challenges we face as a profession and industry.
This part of our talk covers some of my favorite topics.
The speaker will cover a few of the more challenging ones, including problem-solving and problem-solving architectures, and a lot that is new and exciting.
In addition, the talk also talks about some areas of AI that might help us to tackle some of that stuff, and some areas that are less relevant to CAID right now, but are still relevant to the future.
Topics covered in this talk include:Cognitive and computational thinkingWhat’s a problem-based approach to problem-driven design?
What are some problems where you might want to use computational thinking?
The speaker then goes on to explain how to use this approach to make good decisions, and discuss a couple of interesting examples of problem-specific solutions.
The next section covers the way you can apply computational thought to problems in general, and discusses some of some of different ways in which computational thinking can help you to solve problems.
Topics covered in the last section of the discussion include:Learning and thinking about problemsIn the same vein as previous talks, this one will cover how to solve some problems that are more relevant to AI, but still not very relevant to our current jobs.
Topics discussed in the second half of the section include:Designing and testing software algorithmsThe presenter will also talk about how to make use of CAID, both in practice and in research, to build better solutions.
Topics discussed in this section include…
What is computational thinking and how does it apply to machine intelligence?
What kinds of AI-based problems do you want solve?
What might you want CAID to do to help you solve those problems?
Topics covered this part of this talk will include:Software and machine learningHow do the two work together?
How do we design algorithms to be useful for problems that aren’t yet solved?
Software and artificial learningWhy do we need AI to solve these problems?
What does CAID do for you?
This is a very interesting section.
It is the second part of a longer talk that covers AI as a technology.
This part of that talk covers AI-as-a-technology-in-practice, as it relates to what CAID is actually doing to help us solve our own problems.
It’s also about the ways in the future we may use CAID as a tool to help solve problems in the real world.
Topics addressed include:AI and human interactionWhat are CAID’s strengths and weaknesses?
What would it take to change the way people use AI in practice?
How could AI help us in our everyday lives?
Topics addressed in this part will include…AI and machine intelligenceThe speaker goes into detail about the history of AI, and where it is today.