The current hype around artificial intelligence and machine learning brings to mind Amara’s Law.
We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.
Python tops programming language list
Python has topped the IEEE Spectrum list of top programming languages again this year—extending its lead in the process.
The sources used to compiled the list cover
contexts that include social chatter, open-source code production, and job postings.
Obviously that list of sources isn’t an accurate reflection of what developers are doing day-to-day in organisations. Any list of top programming languages that puts R (#7) above JavaScript (#8) clearly has some methodological challenges. My belief is that the list reflects the current buzz around data science.
However, interest in Python clearly remains high. As it does in R—#7 is impressive for a domain-specific language.
Microsoft Research Open Data
Microsoft Research have released over 50 free data sets via their Open Data site. They include
- 38 million tweets from the 2012 US presidential election
- Profiles of 1 million celebrities (1000 with images)
Are there actually 1 million celebrities now?! Maybe someone can analyze the data to confirm. As in all data science we’ll need to start with firming up our definitions of terms—e.g “celebrity”.
Do you really need machine learning?
I have a lot of sympathy for the view expressed in the following tweet
Good CS expert says: Most firms that thinks they want advanced AI/ML really just need linear regression on cleaned-up data.
— robin hanson (@robinhanson) November 28, 2016
Many organizations who dive into machine learning haven’t even started to extract value from their data.
I understand why they want to get on the train. If you’ve not managed to draw value from your data yet why not just shovel it all into these amazing algorithms and let insights flow out the other end. Jump to the latest technology. Makes sense.
Unfortunately that’s not how data science works. The success of any data science project depends on you understanding your business and your data.
Some things you might do before adopting machine learning
- Work out what data you need to help you manage and improve your business
- Introduce your managers to your data people
- Clean and curate your data
- Put systems in place that give you real-time access to useful simple statistics—averages, totals, trends, etc.
- Use simple statistical tools—like linear regression—to get deeper insights
- Make use of decision science techniques (e.g. game theory)—not everything is amenable to running masses of data through algorithms
If you’re ready for it, machine learning is fantastic. However, if you deploy it as a magic bullet, don’t be surprised when you shoot yourself in the head.
Google reveals performance of its custom AI chips
Google has just release performance data for its Tensor Processing Unit (TPU) custom machine learning chips.
TPUs are
- 15-20x faster than GPUs and CPUs when running deep learning applications
- 30-80x more power efficient (operations per Watt)
- frugal in terms of lines of code required to control them (their deep learning applications are implemented using 100-1500 lines)
Google claim that without these chips they would have to double the number of data centers they run.