At CMU you took no courses in operating systems? Algorithms? Computer hardware or logic? Compilers? Graphics? Databases? Web programming? Distributed systems? Networks? Parallel/HPC? Language theory? Security/crypto?
Because these students will take none of these courses, they will differ significantly from those with a BS in CS. But their AI skills still won't run deep enough to make them expert there either. At best, they'll be conversant with a couple of foci in AI, but not in many other AI areas.
In fact, this program seems custom made to prep for work most typical at Google, Facebook, Microsoft, and not that many others -- doing pattrec forms of ML on large data. Yet they'll lack the skills typical of today's data engineers (basic ML plus HPC/distributed/throughput, networking, and DB /sys admin) or typical of data scientists (nasic ML with a BA in statistics, plus facility with RDBMSes).
Will the absence of these CS skills hamper their competitiveness one day in most mainstream general computing software jobs? I think it probably will.
Therefore, if those with this degree don't spend their entire careers working only in big data areas of AI, they will likely will be at a competitive disadvantage to those with broader skills in CS.
> At CMU you took no courses in operating systems? Algorithms? Computer hardware or logic? Compilers? Graphics? Databases? Web programming? Distributed systems? Networks? Parallel/HPC? Language theory? Security/crypto?
The core that's required in both programs (15-122, 15-128, 15-150, 15-210, 15-213, and 15-251) is very broad and touches pretty much all of those areas. To be clear, hardware design isn't covered there, but the (x86-64) programmer's side of memory management and the CPU is covered well.
Other than algorithms, dedicated courses in all of those areas are offered as electives and you pick some of them. I recall taking OS, security, digital design / RTL (which was actually in the ECE department), web, and logic - but I could have subbed OS with Parallel/HPC, for example. The BS in CS curriculum[1] requires enough free and area electives that students gain depth in several of those areas.
> Because these students will take none of these courses, they will differ significantly from those with a BS in CS.
The BS in AI curriculum[2] only requires two CS-wide electives, so students in that program will indeed have depth in fewer of the areas. This is why these students will receive BS in AI degrees, to differentiate them from those who receive BS in CS degrees. I think you're in agreement with CMU's decision here?
That said, with the broad base of the core classes like 15-213 and the second half of 15-210, plus implementation details covered in the AI/ML courses, I'm sure no graduate of that program would struggle with HPC, networking, or DB/sysadmin in the workplace, or in a graduate program in AI.
Ultimately, there's only so much you can fit into four years, but I'd bet it would be easier for someone from this new program to deepen their skills in those areas, than it would be for most BS in CS graduates to add ML skills.
Because these students will take none of these courses, they will differ significantly from those with a BS in CS. But their AI skills still won't run deep enough to make them expert there either. At best, they'll be conversant with a couple of foci in AI, but not in many other AI areas.
In fact, this program seems custom made to prep for work most typical at Google, Facebook, Microsoft, and not that many others -- doing pattrec forms of ML on large data. Yet they'll lack the skills typical of today's data engineers (basic ML plus HPC/distributed/throughput, networking, and DB /sys admin) or typical of data scientists (nasic ML with a BA in statistics, plus facility with RDBMSes).
Will the absence of these CS skills hamper their competitiveness one day in most mainstream general computing software jobs? I think it probably will.
Therefore, if those with this degree don't spend their entire careers working only in big data areas of AI, they will likely will be at a competitive disadvantage to those with broader skills in CS.