Profile cover photo
Profile photo
Mark Lewis
Professor of Computer Science. Author of Scala textbooks. Ring dynamicist, coder, avid roller skater.
Professor of Computer Science. Author of Scala textbooks. Ring dynamicist, coder, avid roller skater.

Mark's posts

Post has attachment
I firmly believe that AI is the real best hope for cutting healthcare costs in the US, not anything that happens in Washington.

Post has attachment
I have no love for Ruby or other dynamic programming languages, but I can see a lot of logic here when applied to Scala, especially in comparison to Java. The biggest complaint about Java is that it is overly verbose and requires a lot of boilerplate code. Fixing that is a big part of what many people love about Scala. Some of the fix is just having a language that was designed to be less verbose, but some of it is because of implicits in the Scala language.

This implicitness does create a barrier for the novice. I feel it myself when I start using a new library in Scala, but I think this author makes a good point, we spend most of our lives not as novices to a language or library, but as established and informed users. Isn't there a value to picking tools that favor that population?

Post has shared content
> Here we report the results from a large survey of machine learning researchers on their beliefs about progress in AI. Researchers predict AI will outperform humans in many activities in the next ten years, such as translating languages (by 2024), writing high-school essays (by 2026), driving a truck (by 2027), working in retail (by 2031), writing a bestselling book (by 2049), and working as a surgeon (by 2053). Researchers believe there is a 50% chance of AI outperforming humans in all tasks in 45 years and of automating all human jobs in 120 years, with Asian respondents expecting these dates much sooner than North Americans. These results will inform discussion amongst researchers and policymakers about anticipating and managing trends in AI.

via +Roman Yampolskiy

// AI experts estimate that computers will beat human Starcraft players in around 5 years, will talk with convincing speech in 10, will write best selling novels in 30, and will achieve general parity with human performance in 45 years.

Many of these tasks have already had significant defeats (like Poker and Go). The article clarifies that Go needs to be won with human-scale training. From Table S5:

> Defeat the best Go players, training only on as many
games as the best Go players have played. For reference, DeepMind’s AlphaGo has probably played a hundred million games of self-play, while Lee Sedol has probably played 50,000 games in his life.

// The reasoning here is that AlphaGo is better at Go because it has more experience than its human counterparts, giving it a profound advantage over any human player.

To me, the interesting thing here is how we're crafting the bounds of what constitutes "human level performance" as a kind of defensive reaction against the encroaching machine.

Post has attachment
This seems like a rather accurate list to me.

Post has attachment
More output doesn't mean more jobs.

Post has attachment
Feedback loops are critical in the workings of the human mind. It is good to see that they are being explored in neural networks as well.

Post has shared content
Google’s speech recognition technology now has a 4.9% word error rate

Google CEO Sundar Pichai today announced that the company’s speech recognition technology has now achieved a 4.9 percent word error rate. Put another way, Google transcribes every 20th word incorrectly. That’s a big improvement from the 23 percent the company saw in 2013 and the 8 percent it shared two years ago at I/O 2015. The tidbit was revealed at Google’s I/O 2017 developer conference, where a big emphasis is on artificial intelligence. Deep learning, a type of AI, is used to achieve accurate image recognition and speech recognition. The method involves ingesting lots of data to train systems called neural networks, and then feeding new data to those systems in an attempt to make predictions. “We’ve been using voice as an input across many of our products,” Pichai said onstage. “That’s because computers are getting much better at understanding speech. We have had significant breakthroughs, but the pace even since last year has been pretty amazing to see. Our word error rate continues to improve even in very noisy environments. This is why if you speak to Google on your phone or Google Home, we can pick up your voice accurately.”

Post has attachment
VR/AR is one of my favorite areas, but one that I've never had much time to get deeply involved in. I really look forward to the day when there is a product in the AR space that I can make good use of. I already use both Cardboard Camera and Google Photo Spheres to document my travels. I find that viewing them afterwards is immersive and lets me re-experience the feelings from the trip.

Post has attachment
This seems like a pretty good list to me. It also pointed me to, which I hadn't been familiar with. I'm a huge fan of keyboard shortcuts though, so I am going to have to try it out.

Post has attachment
I have to admit that I'm in the group of people who is really excited about VR and AR. I firmly believe that they will have a profound impact on our society, once the technology is ready. I agree that it isn't there yet. That doesn't mean it isn't useful though. I use a combination of Google PhotoSpheres and Cardboard Camera pictures to document my travels these days, and one of the things that I really love about it is that I can put on a simple headset (I have a Merge 360) with my phone in it and get enough immersion that I feel like I am back there. It is far from perfect in many ways, but it does a great job of bringing back memories.
Wait while more posts are being loaded