Alongside its Gemini generative AI model, Google this morning took the wraps off of AlphaCode 2, an improved version of the code-generating AlphaCode introduced by Google’s DeepMind lab roughly a year ago.
AlphaCode 2 can understand programming challenges involving “complex” math and theoretical computer science.
And, among other reasonably sophisticated techniques, AlphaCode 2 is capable of dynamic programming, explains DeepMind research scientist Rémi Leblond in a prerecorded video.
Dynamic programming entails simplifying a complex problem by breaking it down into easier sub-problems over and over; Leblond says that AlphaCode 2 knows not only when to properly implement this strategy but where to use it.
According to the whitepaper, AlphaCode 2 requires a lot of trial and error, is too costly to operate at scale and relies heavily on being able to filter out obviously bad code samples.
“One of the things that was most exciting to me about the latest results is that when programmers collaborate with [AlphaCode 2 powered by] Gemini, by defining certain properties for the code to follow, the performance [of the model] gets even better,” Collins said.
The original article contains 567 words, the summary contains 181 words. Saved 68%. I’m a bot and I’m open source!
This is the best summary I could come up with:
Alongside its Gemini generative AI model, Google this morning took the wraps off of AlphaCode 2, an improved version of the code-generating AlphaCode introduced by Google’s DeepMind lab roughly a year ago.
AlphaCode 2 can understand programming challenges involving “complex” math and theoretical computer science.
And, among other reasonably sophisticated techniques, AlphaCode 2 is capable of dynamic programming, explains DeepMind research scientist Rémi Leblond in a prerecorded video.
Dynamic programming entails simplifying a complex problem by breaking it down into easier sub-problems over and over; Leblond says that AlphaCode 2 knows not only when to properly implement this strategy but where to use it.
According to the whitepaper, AlphaCode 2 requires a lot of trial and error, is too costly to operate at scale and relies heavily on being able to filter out obviously bad code samples.
“One of the things that was most exciting to me about the latest results is that when programmers collaborate with [AlphaCode 2 powered by] Gemini, by defining certain properties for the code to follow, the performance [of the model] gets even better,” Collins said.
The original article contains 567 words, the summary contains 181 words. Saved 68%. I’m a bot and I’m open source!